<reponame>hustwyk/MyARDemo
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@INPROCEEDINGS{SURF,
  author = {<NAME> and <NAME> and <NAME>},
  title = {SURF: Speeded Up Robust Features},
  booktitle = {ECCV (1)},
  year = {2006},
  pages = {404-417},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  crossref = {DBLP:conf/eccv/2006-1},
  ee = {http://dx.doi.org/10.1007/11744023_32}
}

@INPROCEEDINGS{REVIEW_HLAC_early,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Memory-Based Location Estimation and Navigation Using Bayesian Estimation},
  booktitle = {ICONIP},
  year = {1998},
  pages = {112-117},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  crossref = {DBLP:conf/iconip/1998}
}

@INPROCEEDINGS{Schulz-mirbach95invariantfeatures,
  author = {<NAME>},
  title = {Invariant Features for Gray Scale Images},
  booktitle = {DAGM - Symposium \&quot;Mustererkennung},
  year = {1995},
  pages = {1--14},
  publisher = {Springer}
}

@ARTICLE{VERTICAL_LINES_1,
  author = {<NAME> and <NAME>},
  title = {Real-time vision-based robot localization},
  journal = {IEEE International Conference on Robotics and Automation},
  year = {1991},
  pages = {639-644 vol.1},
  doi = {10.1109/ROBOT.1991.131654},
  keywords = {computer vision, computerised navigation, computerised pattern recognition,
	mobile robots, real-time systemscomputer vision, geometric tolerance,
	mobile robot, navigation, observation error, preloaded map, real-time
	systems, vision-based robot localization}
}

@ARTICLE{NORMALISATION,
  author = {<NAME> and <NAME>},
  title = {Effects of Feature Normalization on Image Retrieval},
  year = {1999},
  url = {citeseer.ist.psu.edu/260758.html}
}

@INPROCEEDINGS{Wavefront,
  author = {<NAME> and <NAME>},
  title = {Wavefront Propagation and Fuzzy Based Autonomous Navigation},
  booktitle = {International Journal of Advanced Robotic Systems},
  year = {2005},
  volume = {2},
  month = {June},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0601053}
}

@CONFERENCE{DISTANCE_TRANSFORM_BINARY,
  author = {<NAME> and <NAME>},
  title = {On the Distance Transform of Binary Images},
  booktitle = {International Conference on Imaging Science, Systems, and Technology},
  year = {2000.},
  owner = {nghia},
  timestamp = {2008.06.05}
}

@ARTICLE{SAMPLING_BASED_2,
  author = {<NAME>. and <NAME>.},
  title = {A randomized roadmap method for path and manipulation planning},
  journal = {Robotics and Automation, 1996. Proceedings., 1996 IEEE International
	Conference on},
  year = {1996},
  volume = {1},
  pages = {113-120 vol.1},
  month = {Apr},
  doi = {10.1109/ROBOT.1996.503582},
  keywords = {manipulators, optimisation, path planning, random processesC-obstacle
	surfaces, C-space, collision-free path planning, contact tasks, manipulation
	planning, many DOF robots, motion planning, planar articulated 6
	DOF robot, randomized roadmap method}
}

@ARTICLE{3D_MODELLING_Arayici2007816,
  author = {<NAME>},
  title = {An approach for real world data modelling with the 3D terrestrial
	laser scanner for built environment},
  journal = {Automation in Construction},
  year = {2007},
  volume = {16},
  pages = {816 - 829},
  number = {6},
  doi = {DOI: 10.1016/j.autcon.2007.02.008},
  issn = {0926-5805},
  keywords = {3D laser scanner},
  url = {http://www.sciencedirect.com/science/article/B6V20-4NGBB3P-1/2/b94585e6d28517ee65d1d7bf26adf1ca}
}

@ARTICLE{ARKIN,
  author = { <NAME>.},
  title = {Motor schema based navigation for a mobile robot: An approach to
	programming by behavior},
  journal = {Robotics and Automation. Proceedings. 1987 IEEE International Conference
	on},
  year = {1987},
  volume = {4},
  pages = { 264-271},
  month = {Mar}
}

@ARTICLE{MHL_1,
  author = {<NAME>. and <NAME>. and <NAME>.},
  title = {Feature-based multi-hypothesis localization and tracking for mobile
	robots using geometric constraints},
  journal = {Robotics and Automation, 2002. Proceedings. ICRA '02. IEEE International
	Conference on},
  year = {2002},
  volume = {2},
  pages = { 1371-1377 vol.2},
  doi = {10.1109/ROBOT.2002.1014734},
  issn = { },
  keywords = {Kalman filters, computational geometry, mobile robots, navigation,
	position control, probability, ray tracing, state estimationKalman
	filter, constraint based search, geometric constraints, interpretation
	tree, mobile robots, multiple hypothesis global localization, odometry,
	pose tracking, position control, probabilistic feature-based localization,
	ray tracing, state estimation}
}

@INPROCEEDINGS{REVIEW_PCA_2,
  author = {<NAME>. and <NAME>. and <NAME>. and <NAME>.},
  title = {Panoramic volumes for robot localization},
  booktitle = {Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ
	International Conference on},
  year = {2005},
  pages = { 2668-2674},
  month = {Aug.},
  abstract = { We propose a method for visual robot localization using a panoramic
	image volume as the representation from which we can generate views
	from virtual viewpoints and match them to the current view. We use
	a geometric image-based rendering formalism in combination with a
	subspace representation of images, which allows us to synthesize
	views at arbitrary virtual viewpoints from a compact low-dimensional
	representation.},
  doi = {10.1109/IROS.2005.1545557},
  keywords = { image representation, mobile robots, rendering (computer graphics),
	robot vision, stereo image processing geometric image-based rendering,
	image representation, mobile robots, panoramic image volume, subspace
	representation, virtual viewpoint, visual robot localization}
}

@ARTICLE{ICP_ARUN,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Least-squares fitting of two 3-D point sets},
  journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
  year = {1987},
  volume = {9},
  pages = {698--700},
  number = {5},
  address = {Washington, DC, USA},
  issn = {0162-8828},
  publisher = {IEEE Computer Society}
}

@ARTICLE{ANN,
  author = {<NAME> and <NAME> and <NAME> and <NAME>
	and <NAME>},
  title = {An optimal algorithm for approximate nearest neighbor searching fixed
	dimensions},
  journal = {J. ACM},
  year = {1998},
  volume = {45},
  pages = {891--923},
  number = {6},
  address = {New York, NY, USA},
  doi = {http://doi.acm.org/10.1145/293347.293348},
  issn = {0004-5411},
  publisher = {ACM}
}

@ARTICLE{3D_MODELLING_Axelsson1999138,
  author = {<NAME>},
  title = {Processing of laser scanner data--algorithms and applications},
  journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
  year = {1999},
  volume = {54},
  pages = {138 - 147},
  number = {2-3},
  doi = {DOI: 10.1016/S0924-2716(99)00008-8},
  issn = {0924-2716},
  keywords = {laser scanner},
  url = {http://www.sciencedirect.com/science/article/B6VF4-3WY9SWX-B/2/ee2a8bfdb0b822fd2de5522d8f3c27d3}
}

@ARTICLE{BLIND_2,
  author = {<NAME>. and <NAME> <NAME>.},
  title = {A GPS based navigation aid for the blind},
  journal = {Applied Electromagnetics and Communications, 2003. ICECom 2003. 17th
	International Conference on},
  year = {2003},
  pages = { 34-36},
  month = {Oct.},
  abstract = { In this paper we present development of the GPS based navigation
	aid for blind. That aid utilizes inverse DGPS positioning module
	which, along with other benefits for the blind user, enables implementation
	of more complex navigation algorithms. In particular, we have implemented
	DGPS algorithm augmented by altitude data available from digital
	map. This solution improved the availability of positioning by more
	that 23% and its accuracy by more than 15%. All the results were
	experimentally verified.},
  doi = {10.1109/ICECOM.2003.1290948},
  issn = { },
  keywords = { Global Positioning System, handicapped aids GPS based navigation
	aid, blind, blind user, digital map, inverse DGPS position, navigation
	algorithm}
}

@INPROCEEDINGS{SIFT_6,
  author = {<NAME> and <NAME> and <NAME> and <NAME>},
  title = {Metric Localization with Scale-Invariant Visual Features Using a
	Single Perspective Camera},
  booktitle = {In ProceedingsofEuropeanRoboticsSymposium(EUROS-06},
  year = {2006},
  pages = {143--157}
}

@ARTICLE{ICP,
  author = {<NAME> and <NAME>},
  title = {A Method for Registration of 3-D Shapes},
  journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
  year = {1992},
  volume = {14},
  pages = {239--256},
  number = {2},
  address = {Washington, DC, USA},
  doi = {http://dx.doi.org/10.1109/34.121791},
  issn = {0162-8828},
  publisher = {IEEE Computer Society}
}

@INPROCEEDINGS{VORONOI_1,
  author = {<NAME> and <NAME>},
  title = {Voronoi diagram in optimal path planning},
  booktitle = {ISVD '07: Proceedings of the 4th International Symposium on Voronoi
	Diagrams in Science and Engineering},
  year = {2007},
  pages = {38--47},
  address = {Washington, DC, USA},
  publisher = {IEEE Computer Society},
  doi = {http://dx.doi.org/10.1109/ISVD.2007.43},
  isbn = {0-7695-2869-4}
}

@ARTICLE{COLOUR_HISTOGRAM_2,
  author = {<NAME> and <NAME>},
  title = {Topological mobile robot localization using fast vision techniques},
  journal = {Robotics and Automation, 2002. Proceedings. ICRA '02. IEEE International
	Conference on},
  year = {2002},
  volume = {1},
  pages = { 1031-1036 vol.1},
  doi = {10.1109/ROBOT.2002.1013491},
  issn = { },
  keywords = {image colour analysis, image matching, mobile robots, path planning,
	robot visionAVENUE mobile robot, autonomous vehicle, color histogram
	matching, exploration, fast vision techniques, navigation, navigational
	tool, omnidirectional images, outdoor environment, rotation invariant
	method, topological mobile robot localization, urban environments,
	vision systems}
}

@INPROCEEDINGS{ADAPTIVE_PARTICLE_Boers99,
  author = {<NAME>.},
  title = {On the number of samples to be drawn in particle filtering},
  booktitle = {Target Tracking: Algorithms and Applications (Ref. No. 1999/090,
	1999/215), IEE Colloquium on},
  year = {1999},
  pages = {5/1-5/6},
  keywords = { tracking filters 3D target tracking, Bayes rule, Chebyshev bound,
	Gaussian process, bootstrap algorithm, bounds for sample size, computational
	load, discrete time model, filter algorithm, inequalities, level
	of confidence, noisy measurements, nonlinear filtering problem, particle
	filtering, probability theory, required number of samples, sampling
	filters, state estimation, state space form, statistical learning
	theory, stochastic dynamical system}
}

@ARTICLE{ENBLEND_1,
  author = {<NAME> and <NAME>},
  title = {A Multiresolution Spline with Application to Image Mosaics},
  journal = {ACM Transactions on Graphics},
  year = {1983},
  volume = {2},
  pages = {217-236},
  number = {4},
  url = {citeseer.ist.psu.edu/burt83multiresolution.html}
}

@ARTICLE{ENBLEND_2,
  author = {<NAME> and <NAME>},
  title = {The Laplacian Pyramid as a compact image code},
  journal = {IEEE Transactions on Communications},
  year = {1983},
  volume = {COM-31,4},
  pages = {532--540},
  publisher = {IEEE},
  url = {citeseer.ist.psu.edu/burt83laplacian.html}
}

@ARTICLE{SLAM_3,
  author = {<NAME> and <NAME> and <NAME> and <NAME>},
  title = {The SPmap: A probabilistic framework for simultaneous localization
	and map building},
  journal = {IEEE Transactions on Robotics and Automation},
  year = {1999},
  volume = {15},
  pages = {948--953}
}

@MISC{SHAKEY,
  author = {SRI International's Artificial Intelligence Center},
  title = {Shakey},
  year = {1966},
  note = {http://www.ai.sri.com/shakey/},
  owner = {nghia},
  timestamp = {2008.12.17}
}

@ARTICLE{panoramicmirror,
  author = {<NAME>},
  title = {Panoramic surveillance system},
  journal = {U.S. Patent 5,790,181},
  year = {1998}
}

@INPROCEEDINGS{chetverikov02trimmed,
  author = {<NAME> and <NAME> and <NAME> and <NAME>},
  title = {The Trimmed Iterative Closest Point Algorithm},
  booktitle = {Proc. International Conf. on Pattern Recognition, Quebec, Canada.
	IEEE Comp. Soc.},
  year = {2002}
}

@INPROCEEDINGS{REVIEW_INVARIANTS_3,
  author = {<NAME>, <NAME>, <NAME> and <NAME>},
  title = {Fast Outdoor Robot Localization Using Integral Invariants},
  booktitle = {Proceedings of the 5th International Conference on Computer Vision
	Systems (ICVS 2007)},
  year = {2007},
  month = {March},
  owner = {nghia},
  timestamp = {2009.07.21}
}

@INPROCEEDINGS{Corke_2004_4913,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Omnidirectional Visual Odometry for a Planetary Rover},
  booktitle = {Proceedings of International Conference on Intelligent Robots and
	Systems (IROS) 2004},
  year = {2004}
}

@MISC{ROOMBA,
  author = {iRobot Corporation},
  title = {Roomba Vacuuming Robot},
  year = {2002},
  note = {http://www.roomba.com.au},
  owner = {nghia},
  timestamp = {2008.12.17}
}

@INPROCEEDINGS{REVIEW_HARRIS_2,
  author = {<NAME> <NAME> <NAME> <NAME>.},
  title = {Efficient hierarchical localization method in an omnidirectional
	images memory},
  booktitle = {Robotics and Automation, 2008. ICRA 2008. IEEE International Conference
	on},
  year = {2008},
  pages = {13-18},
  month = {May},
  abstract = {An efficient method for global robot localization in a memory of omnidirectional
	images is presented. This method is valid for indoor and outdoor
	environments and not restricted to mobile robots. The proposed strategy
	is purely vision-based and uses as reference a set of prerecorded
	images (visual memory). The localization consists on finding in the
	visual memory the image which best fits the current image. We propose
	a hierarchical process combining global descriptors computed onto
	cubic interpolation of triangular mesh and patches correlation around
	Harris corners. To evaluate this method, three large images data
	sets have been used. Results of the proposed method are compared
	with those obtained from state-of-the-art techniques by means of
	1) accuracy, 2) amount of memorized data required per image and 3)
	computational cost. The proposed method shows the best compromise
	in term of those criteria.},
  doi = {10.1109/ROBOT.2008.4543180},
  issn = {1050-4729},
  keywords = {interpolation, mesh generation, mobile robots, robot visionHarris
	corners, cubic interpolation, efficient hierarchical localization
	method, global robot localization, mobile robots, omnidirectional
	images memory, triangular mesh, visual memory}
}

@ARTICLE{COX,
  author = {<NAME>.},
  title = {Blanche-an experiment in guidance and navigation of an autonomous
	robot vehicle},
  journal = {Robotics and Automation, IEEE Transactions on},
  year = {1991},
  volume = {7},
  pages = {193-204},
  number = {2},
  month = {Apr},
  doi = {10.1109/70.75902},
  issn = {1042-296X},
  keywords = {automatic guided vehicles, industrial robots, position controlAGV,
	Blanche, autonomous robot vehicle, closed-loop motion control, collision
	avoidance, collision-free maneuvers, factory environments, low-level
	trajectory generator, maximum-likelihood combination, odometry, office
	environment, offline path planner, robust matching algorithm, structured
	environment}
}

@INPROCEEDINGS{REVIEW_SEXTANT,
  author = {<NAME>. and <NAME>.},
  title = {Robot localization using a computer vision sextant},
  booktitle = {Robotics and Automation, 1995. Proceedings., 1995 IEEE International
	Conference on},
  year = {1995},
  volume = {1},
  pages = {106-111 vol.1},
  month = {May},
  abstract = {This paper explores the possibility of using Sun altitude for localization
	of a robot in totally unknown territory. A set of Sun altitudes is
	obtained by processing a sequence of time-indexed images of the sky.
	Each altitude constrains the viewer to a circle on the surface of
	a celestial body, called the circle of equal altitude. A set of circles
	of equal altitude can be intersected to yield viewer position. We
	use this principle to obtain the position on Earth. Since altitude
	measurements are corrupted by noise, a least-square estimate is numerically
	calculated from the sequence of altitudes. The paper discusses the
	necessary theory for Sun-based localization, the technical issues
	of camera calibration and image processing, and presents preliminary
	results with real data},
  doi = {10.1109/ROBOT.1995.525271},
  issn = {1050-4729},
  keywords = { Sun, aerospace control, calibration, height measurement, image sequences,
	least squares approximations, mobile robots, navigation, position
	control, robot vision Sun altitude, Sun-based localization, camera
	calibration, celestial body, circle of equal altitude, computer vision
	sextant, image processing, least-square estimate, robot localization,
	time-indexed image sequences}
}

@INPROCEEDINGS{SIR,
  author = {<NAME>.},
  title = {Using the SIR algorithm to simulate posterior distributions},
  booktitle = {Bayesian Statistics 3},
  year = {1988},
  editor = {<NAME>, <NAME>, <NAME> et <NAME>},
  publisher = {Oxford University Press},
  chapter = {Using the SIR Algorithm to Simulate Posterior Distributions},
  owner = {nghia},
  timestamp = {2008.06.04}
}

@ARTICLE{HOG,
  author = {<NAME>. and <NAME>.},
  title = {Histograms of oriented gradients for human detection},
  journal = {Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer
	Society Conference on},
  year = {2005},
  volume = {1},
  pages = {886-893 vol. 1},
  month = {June},
  doi = {10.1109/CVPR.2005.177},
  issn = {1063-6919},
  keywords = {feature extraction, gradient methods, object detection, object recognition,
	support vector machinescoarse spatial binning, contrast normalization,
	edge based descriptors, fine orientation binning, fine-scale gradients,
	gradient based descriptors, histograms of oriented gradients, human
	detection, linear SVM, overlapping descriptor, pedestrian database,
	robust visual object recognition}
}

@INPROCEEDINGS{REVIEW_BAG_OF_WORDS_1,
  author = {<NAME> and Willamowski, Jutta and <NAME> and Bray, Cedric
	and Csurka, Gabriela},
  title = {Visual categorization with bags of keypoints},
  booktitle = {ECCV International Workshop on Statistical Learning in Computer Vision},
  year = {2004},
  abstract = {We present a method for generic visual categorization. This technique
	exploits an analogy with learning methods for text categorization
	based on the simple bag of words approach. Two key novel aspects
	of this approach are that it handles multiple image categories simultaneously
	and that it is intrinsically invariant to affine image transformations.
	Results are presented for simultaneously classifying seven semantic
	visual categories using Naive Bayes techniques.},
  citeulike-article-id = {581127},
  citeulike-linkout-0 = {http://www.xrce.xerox.com/Publications/Attachments/2004-010/2004_010.pdf},
  keywords = {bag-of-patches, caltech, classification, recognition, vision},
  posted-at = {2006-04-10 16:28:58},
  priority = {4},
  url = {http://www.xrce.xerox.com/Publications/Attachments/2004-010/2004_010.pdf}
}

@INPROCEEDINGS{JARVIS_ROBOTIC_PATH_PLANNING_USING_RRT,
  author = {<NAME> and <NAME>},
  title = {Robotic Path Planning Using Rapidly Exploring Random Trees},
  booktitle = {Australian Conference on Robotics and Automation},
  year = {2003},
  month = {Decemeber},
  owner = {nghia},
  timestamp = {2010.12.17}
}

@ARTICLE{MCL_1,
  author = {<NAME> and <NAME> and <NAME> and <NAME>},
  title = {Using the Condensation Algorithm for Robust, Vision-based Mobile
	Robot Localization},
  journal = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition
	( CVPR'99 )},
  year = {1999},
  month = {June}
}

@ARTICLE{MCL_2,
  author = {<NAME> and <NAME> and <NAME> and <NAME>},
  title = {Monte Carlo localization for mobile robots},
  journal = {IEEE International Conference on Robotics and Automation (ICRA99)},
  year = {1999},
  volume = {2},
  pages = {1322-1328 vol.2},
  doi = {10.1109/ROBOT.1999.772544},
  keywords = {Monte Carlo methods, distance measurement, mobile robots, path planning,
	probabilityMonte Carlo localization, arbitrary distributions, indoor
	environments, position estimation, probability densities, sampling-based
	representation}
}

@MISC{UNIMATE_1,
  author = {<NAME>},
  title = {1961: The first robot},
  year = {1961},
  note = {http://www.capitalcentury.com/1961.html},
  owner = {nghia},
  timestamp = {2008.12.17}
}

@MISC{UNIMATE_2,
  author = {<NAME>},
  title = {The Robot Hall of Fame: Unimate},
  year = {1961},
  note = {http://www.robothalloffame.org/unimate.html},
  owner = {nghia},
  timestamp = {2008.12.17}
}

@INPROCEEDINGS{Dijkstra,
  author = {<NAME>. },
  title = {A note on two problems in connexion with graphs},
  booktitle = {Numerische Mathematik},
  year = {1959},
  volume = {1},
  number = {1},
  pages = {269--271},
  month = {December},
  citeulike-article-id = {2215313},
  doi = {10.1007/BF01386390},
  journal = {Numerische Mathematik},
  keywords = {graph, mathematics},
  posted-at = {2008-01-10 16:41:14},
  priority = {2},
  url = {http://dx.doi.org/10.1007/BF01386390}
}

@INPROCEEDINGS{PSM,
  author = {<NAME>. and <NAME>.},
  title = {Laser scan matching in polar coordinates with application to SLAM},
  booktitle = {Intelligent Robots and Systems},
  year = {2005},
  pages = { 3317-3322},
  month = {Aug.},
  doi = {10.1109/IROS.2005.1545181},
  journal = {Intelligent Robots and Systems, 2005. (IROS 2005). 2005 IEEE/RSJ
	International Conference on},
  keywords = { Kalman filters, image matching, path planning 2D laser scan matching,
	Kalman filter, SLAM, mapping, polar scan matching, simultaneous localization}
}

@ARTICLE{SLAM_1,
  author = {Dissanayake, M.W.M.G. and <NAME>. and <NAME>. and Durrant-Whyte,
	H.F. and <NAME>.},
  title = {A solution to the simultaneous localization and map building (SLAM)
	problem},
  journal = {Robotics and Automation, IEEE Transactions on},
  year = {2001},
  volume = {17},
  pages = {229-241},
  number = {3},
  month = {Jun},
  doi = {10.1109/70.938381},
  issn = {1042-296X},
  keywords = {covariance matrices, estimation theory, filtering theory, mobile robots,
	path planning, state estimationSLAM problem, absolute accuracy, absolute
	vehicle location, autonomous vehicle, estimation-theoretic foundations,
	map management, millimeter-wave radar, outdoor environment, perfect
	map, relative map, simultaneous localization and map building problem,
	suboptimal map-building algorithms, unknown environment, unknown
	location}
}

@MISC{DARPA,
  author = {DRAPA},
  title = {DARPA Urban Challenge},
  year = {2004},
  note = {http://www.darpa.mil/grandchallenge},
  owner = {nghia},
  timestamp = {2008.12.17}
}

@MISC{BIGDOG,
  author = {<NAME>},
  title = {BigDog},
  year = {2005},
  note = {\linebreak http://www.bostondynamics.com/content/sec.php?section=BigDog},
  owner = {nghia},
  timestamp = {2008.12.17}
}

@ARTICLE{BLIND,
  author = {<NAME>. and <NAME> <NAME>. and <NAME>.},
  title = {Probabilistic localization with a blind robot},
  journal = {Robotics and Automation, 2008. ICRA 2008. IEEE International Conference
	on},
  year = {2008},
  pages = {1821-1827},
  month = {May},
  abstract = {Researchers have addressed the localization problem for mobile robots
	using many different kinds of sensors, including rangefinders, cameras,
	and odometers. In this paper, we consider localization using a robot
	that is virtually "blind", having only a clock and contact sensor
	at its disposal. This represents a drastic reduction in sensing requirements,
	even in light of existing work that considers localization with limited
	sensing. We present probabilistic techniques that represent and update
	the robot's position uncertainty and algorithms to reduce this uncertainty.
	We demonstrate the experimental effectiveness of these methods using
	a Roomba autonomous vacuum cleaner robot in laboratory environments.},
  doi = {10.1109/ROBOT.2008.4543472},
  issn = {1050-4729},
  keywords = {mobile robots, position control, probabilityblind robot, clock-and-contact
	sensor, mobile robot, probabilistic localization, robot position
	uncertainty}
}

@ARTICLE{eth_biwi_00621,
  author = {<NAME> and <NAME>},
  title = {Interactive Museum Guide: Accurate Retrieval of Object Descriptions},
  journal = {Adaptive Multimedia Retrieval: User, Context, and Feedback, 4th International
	Workshop, AMR 2006, Lecture Notes in Computer Science (LNCS)},
  year = {2007},
  volume = {4398},
  pages = {179-191},
  keywords = {Interactive Museum Guide, Foveation, Gaussian Image Intensity Attenuation,
	Multiple matches}
}

@INPROCEEDINGS{REVIEW_BAG_OF_WORDS_2,
  author = {<NAME>.},
  title = {A visual bag of words method for interactive qualitative localization
	and mapping},
  booktitle = {Robotics and Automation, 2007 IEEE International Conference on},
  year = {2007},
  pages = {3921-3926},
  month = {April},
  abstract = {Localization for low cost humanoid or animal-like personal robots
	has to rely on cheap sensors and has to be robust to user manipulations
	of the robot. We present a visual localization and map-learning system
	that relies on vision only and that is able to incrementally learn
	to recognize the different rooms of an apartment from any robot position.
	This system is inspired by visual categorization algorithms called
	bag of words methods that we modified to make fully incremental and
	to allow a user-interactive training. Our system is able to reliably
	recognize the room in which the robot is after a short training time
	and is stable for long term use. Empirical validation on a real robot
	and on an image database acquired in real environments are presented.},
  doi = {10.1109/ROBOT.2007.364080},
  issn = {1050-4729},
  keywords = {SLAM (robots), humanoid robots, image recognition, learning (artificial
	intelligence), robot visionanimal-like personal robots, humanoid
	robots, incremental learning, interactive mapping, interactive qualitative
	localization, map-learning system, robot vision, room recognition,
	user-interactive training, visual bag of words method, visual categorization}
}

@ARTICLE{QUADTREES,
  author = {<NAME> and <NAME>},
  title = {Quad trees a data structure for retrieval on composite keys},
  journal = {Acta Informatica},
  year = {1974},
  volume = {Volume 4, Number 1},
  pages = {1-9},
  owner = {nghia},
  timestamp = {2008.07.20}
}

@ARTICLE{RANSAC,
  author = {<NAME> and <NAME>},
  title = {Random sample consensus: a paradigm for model fitting with applications
	to image analysis and automated cartography},
  journal = {Commun. ACM},
  year = {1981},
  volume = {24},
  pages = {381--395},
  number = {6},
  address = {New York, NY, USA},
  doi = {http://doi.acm.org/10.1145/358669.358692},
  issn = {0001-0782},
  publisher = {ACM}
}

@PHDTHESIS{MARKOV_THESIS,
  author = {<NAME>},
  title = {Markov Localization: A Probabilistic Framework for Mobile Robot Localization
	and Navigation},
  school = {University of Bonn, Germany},
  year = {December 1998},
  owner = {nghia},
  timestamp = {2008.06.05}
}

@INPROCEEDINGS{Fox01KLD,
  author = {<NAME>},
  title = {KLD-Sampling: Adaptive Particle Filters},
  booktitle = {Advances in Neural Information Processing Systems 14},
  year = {2001},
  publisher = {MIT Press}
}

@ARTICLE{MARKOV_1,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Markov Localization for Mobile Robots in Dynamic Environments},
  journal = {Journal of Artificial Intelligence Research},
  year = {1999},
  volume = {11},
  pages = {391-427},
  url = {citeseer.ist.psu.edu/fox99markov.html}
}

@INCOLLECTION{Fox00Par,
  author = {<NAME>. and <NAME>. and <NAME>. and <NAME>.},
  title = {Particle filters for mobile robot localization},
  booktitle = {Sequential {M}onte {C}arlo Methods in Practice},
  publisher = {Springer Verlag},
  year = {2000},
  editor = {<NAME>. and <NAME>. and <NAME>.},
  address = {New York},
  note = {To appear}
}

@INPROCEEDINGS{REVIEW_HARRIS,
  author = {<NAME>. and <NAME>. and <NAME>.},
  title = {Pay Attention When Selecting Features},
  booktitle = {Pattern Recognition, 2006. ICPR 2006. 18th International Conference
	on},
  year = {2006},
  volume = {2},
  pages = {163-166},
  month = {0-0 },
  abstract = {In this paper, we propose a new, hierarchical approach to landmark
	selection for simultaneous robot localization and mapping based on
	visual sensors: a biologically motivated attention system finds salient
	regions of interest (ROIs) in images, and within these regions, Harris
	corners are detected. This combines the advantages of the ROIs (reducing
	complexity, enabling good redetectability of regions) with the advantages
	of the Harris corners (high stability). Reducing complexity is important
	to meet real-time requirements and stability of features is essential
	to compute the depth of landmarks from structure from motion with
	a small baseline. We show that the number of landmarks is highly
	reduced compared to all Harris corners while maintaining the stability
	of features for the mapping task},
  doi = {10.1109/ICPR.2006.922},
  issn = {1051-4651},
  keywords = {control engineering computing, image motion analysis, image sensors,
	object detection, robot visionHarris corner detection, biologically
	motivated attention system, complexity reduction, landmark selection,
	robot, salient regions of interest, simultaneous localization and
	mapping, visual sensors}
}

@ARTICLE{3D_MODELLING_Fascade,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Data Processing Algorithms for Generating Textured 3D Building Facade
	Meshes from Laser Scans and Camera Images},
  journal = {Int. J. Comput. Vision},
  year = {2005},
  volume = {61},
  pages = {159--184},
  number = {2},
  address = {Hingham, MA, USA},
  doi = {http://dx.doi.org/10.1023/B:VISI.0000043756.03810.dd},
  issn = {0920-5691},
  publisher = {Kluwer Academic Publishers}
}

@INPROCEEDINGS{3D_MODELLING_ground_airborne,
  author = {<NAME>. and <NAME>.},
  title = {Constructing 3D city models by merging ground-based and airborne
	views},
  booktitle = {Computer Vision and Pattern Recognition, 2003. Proceedings. 2003
	IEEE Computer Society Conference on},
  year = {2003},
  volume = {2},
  pages = { II-562-9 vol.2},
  month = {June},
  doi = {10.1109/CVPR.2003.1211517},
  issn = {1063-6919 },
  keywords = { Monte Carlo methods, digital simulation, image texture, mesh generation,
	solid modelling, stereo image processing, terrain mapping, town and
	country planning 3D city model, 3D city model construction, DSM triangulation,
	Digital Surface Map, Monte-Carlo-Localization, aerial imagery, airborne
	laser scan, airborne laser scans, airborne view, birds eye view,
	close-range facade model, digital camera, downtown Berkeley, ground-based
	view, laser scanner, normal traffic condition, public road, roof
	shape, scan matching, terrain shape, texture mapping, textured 3D
	model, urban environment, urban simulation}
}

@ARTICLE{3D_funkhouser96uc,
  author = {<NAME> and <NAME> and <NAME> and <NAME>},
  title = {The {UC} Berkeley System for Interactive Visualization of Large Architectural
	Models},
  journal = {Presence, the Journal of Virtual Reality and Teleoperators},
  year = {1996},
  volume = {5},
  pages = {13--44},
  number = {1},
  publisher = {MIT Press},
  url = {citeseer.ist.psu.edu/funkhouser96uc.html}
}

@INPROCEEDINGS{ROADMAPS,
  author = {<NAME> and <NAME>},
  title = {A Comparative Study of Probabilistic Roadmap Planners.},
  booktitle = {Workshop on the Algorithmic Foundations of Robotics (WAFR'02)},
  year = {2002},
  owner = {nghia},
  timestamp = {2009.07.20}
}

@INPROCEEDINGS{SIFT_5,
  author = { <NAME>. and <NAME>. and <NAME>. and <NAME>.
	and <NAME>. and <NAME>. and <NAME>.},
  title = {A Visual Front-end for Simultaneous Localization and Mapping},
  booktitle = {Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005
	IEEE International Conference on},
  year = {2005},
  pages = { 44-49},
  month = {April},
  abstract = { We describe a method of generating and utilizing visual landmarks
	that is well suited for SLAM applications. The landmarks created
	are highly distinctive and reliably detected, virtually eliminating
	the data association problem present in other landmark schemes. Upon
	subsequent detections of a landmark, a 3-D pose can be estimated.
	The scheme requires a single camera. },
  keywords = {null SIFT-features , SLAM , landmark detection , pose estimation,
	vision-based landmarks }
}

@ARTICLE{BOOTSTRAP,
  author = {<NAME>. and <NAME>. and <NAME>.},
  title = {Novel approach to nonlinear/non-Gaussian Bayesian state estimation
	},
  journal = {Radar and Signal Processing, IEE Proceedings F},
  year = {1993},
  volume = {140},
  pages = {107-113},
  number = {2},
  month = {Apr},
  issn = {0956-375X},
  keywords = {Bayes methods, Kalman filters, filtering and prediction theory, state
	estimation, trackingGaussian noise, algorithm, bearings only tracking
	problem, bootstrap filter, extended Kalman filter, measurement model,
	nonGaussian Bayesian state estimation, nonlinear Bayesian state estimation,
	random samples, recursive Bayesian filters, simulation, state transition
	model, state vector density}
}

@INPROCEEDINGS{3D_MODELLING_Haala97generationof,
  author = {<NAME> and <NAME>},
  title = {Generation Of 3D City Models From Airborne Laser Scanning Data},
  booktitle = {Proceedings EARSEL workshop on LIDAR remote sensing on land and sea},
  year = {1997},
  pages = {105--112}
}

@INPROCEEDINGS{3D_MODELLING_Haala983durban,
  author = {<NAME> and <NAME> and <NAME>},
  title = {3D Urban GIS From Laser Altimeter And 2D Map Data},
  booktitle = {International Archives of Photogrammetry and Remote Sensing},
  year = {1998},
  pages = {339--346}
}

@ARTICLE{RFID,
  author = {<NAME>. and <NAME>. and <NAME>. and <NAME>. and Philipose,
	M.},
  title = {Mapping and localization with RFID technology},
  journal = {Robotics and Automation, 2004. Proceedings. ICRA '04. 2004 IEEE International
	Conference on},
  year = {2004},
  volume = {1},
  pages = { 1015-1020 Vol.1},
  month = {April-1 May},
  doi = {10.1109/ROBOT.2004.1307283},
  issn = {1050-4729 },
  keywords = { mobile robots, probability, radiofrequency identification RFID technology,
	mobile robot localization, probabilistic measurement model, radio
	frequency identification}
}

@ARTICLE{USER_LOCALIZATION_ELECTRO,
  author = {<NAME>. and <NAME>. and <NAME>.},
  title = {User Localization Using Wearable Electromagnetic Tracker and Orientation
	Sensor},
  journal = {Wearable Computers, 2006 10th IEEE International Symposium on},
  year = {2006},
  pages = {55-58},
  month = {Oct.},
  abstract = {This paper describes a localization method with wearable electromagnetic
	sensor and orientation sensor for wearable computer users. Many user
	localization methods have been investigated to realize location-based
	services in a wide environment. The localization methods usually
	employ a hybrid approach in which user's position is estimated by
	using positioning infrastructures and dead reckoning such as a pedometer.
	However, the installation cost of infrastructures increases when
	the area expands, and the error of the pedometer is frequently caused
	by failures in walking locomotion detection and the difference between
	ideal and estimated step lengths. If the relative distance is accurately
	estimated by dead reckoning approach, the installation cost of infrastructures
	can be reduced. This paper proposes a new localization method that
	improves the estimation accuracy of step length in dead reckoning
	approach. The proposed method measures user's orientation and geometrical
	relationship between user's heel and waist with an orientation sensor
	and an electromagnetic tracker attached to user's body. When both
	feet come into contact with the ground, user's position is updated
	by adding the estimated step length which means the relationship
	between user's heels and the position estimated in the previous step.
	The proposed localization method has been experimented using a prototype
	system to evaluate the accuracy of the proposed method.},
  doi = {10.1109/ISWC.2006.286343},
  issn = {1550-4816},
  keywords = {electric sensing devices, electromagnetic devices, position measurement,
	wearable computerslocation-based services, orientation sensor, pedometer,
	user localization, wearable computer, wearable electromagnetic tracker}
}

@INPROCEEDINGS{HARRIS_CORNER,
  author = {<NAME>. and <NAME>.},
  title = {A combined corner and edge detector},
  booktitle = {Proc. Fourth Alvey Vision Conference},
  year = {1988},
  pages = {147--151},
  abstract = {Consistency of image edge filtering is of prime importance for 3D
	interpretation of image sequences using feature tracking algorithms.
	To cater for image regions containing texture and isolated features,
	a combined corner and edge detector based on the local auto-correlation
	function is utilised, and it is shown to perform with good consistency
	on natural imagery.},
  citeulike-article-id = {4642530},
  keywords = {corner, detectors},
  posted-at = {2009-05-27 09:28:44},
  priority = {0}
}

@ARTICLE{A_STAR,
  author = {<NAME>. and <NAME>. and <NAME>.},
  title = {A Formal Basis for the Heuristic Determination of Minimum Cost Paths},
  journal = {Systems Science and Cybernetics, IEEE Transactions on},
  year = {1968},
  volume = {4},
  pages = {100-107},
  number = {2},
  month = {July },
  doi = {10.1109/TSSC.1968.300136},
  issn = {0536-1567}
}

@INPROCEEDINGS{Ho_ACRA_2008,
  author = {<NAME> and <NAME>},
  title = {Towards a Platform Independent Real-Time Panoramic Vision Based Localisation
	System},
  booktitle = {Australasian Conference on Robotics and Automation (ACRA)},
  year = {2008},
  address = {Canberra, Australia},
  month = {Dec}
}

@INPROCEEDINGS{ACRA2007,
  author = {<NAME> and <NAME>},
  title = {Global Localisation in Real and Cyberworlds Using Vision},
  booktitle = {Australasian Conference on Robotics and Automation (ACRA)},
  year = {2007},
  address = {Brisbane, Australia},
  month = {Dec}
}

@ARTICLE{IROS2008,
  author = {<NAME> and <NAME>.},
  title = {Vision Based Global Localisation Using a 3D Environmental Model Created
	by a Laser Range Scanner},
  journal = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year = {2008},
  owner = {nghia},
  timestamp = {2008.08.28}
}

@ARTICLE{08_Ho07,
  author = {<NAME> and <NAME>.},
  title = {Large Scale 3D Environmental Modelling for Stereoscopic Walk-Through
	Visualisation},
  journal = {3DTV Conference},
  year = {2007}
}

@MISC{ASIMO,
  author = {Honda},
  title = {{ASIMO - The Honda Humoid Robot}},
  year = {2000},
  note = {http://world.honda.com/ASIMO/},
  owner = {nghia},
  timestamp = {2008.12.17}
}

@ARTICLE{ICP_HORN_ETAL,
  author = {<NAME> and <NAME> and <NAME>.},
  title = {Closed-form solution of absolute orientation using orthonormal matrices.
	},
  journal = {Journal of the Optical Society of America A},
  year = {1988},
  pages = {5(7):1127 - 1135}
}

@ARTICLE{ICP_HORN,
  author = {<NAME>.},
  title = {Closed-form solution of absolute orientation using unit quaternions.},
  journal = {Journal of the Optical Society of America A},
  year = {1987},
  pages = {4(4):629-642}
}

@INPROCEEDINGS{REVIEW_HLAC_2_Hotta98scaleinvariant,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Scale Invariant Face Detection Method using Higher-Order Local Autocorrelation
	Features extracted from Log-Polar Image},
  booktitle = {in Automatic Face and Gesture Recognition. Proceedings. Third IEEE
	International Conference on, IEEE},
  year = {1998},
  pages = {70--75}
}

@INPROCEEDINGS{UAV_PANORAMIC,
  author = {<NAME>. and <NAME>.},
  title = {Omnidirectional vision for an autonomous helicopter},
  booktitle = {Robotics and Automation, 2003. Proceedings. ICRA '03},
  year = {2003},
  volume = {1},
  pages = {558-563},
  month = {September},
  abstract = {We present the design and implementation of an omnidirectional vision
	system used for sideways-looking sensing on an autonomous helicopter.
	To demonstrate the capabilities of the system, a visual servoing
	task was designed which required the helicopter to locate and move
	towards the centroid of a number of visual targets. Results are presented
	showing that the task was successfully completed by a Pioneer ground
	robot equipped with the same omnidirectional vision system, and preliminary
	test...},
  citeulike-article-id = {232313},
  keywords = {vision},
  posted-at = {2005-06-20 15:03:12},
  priority = {2},
  url = {http://citeseer.ist.psu.edu/hrabar03omnidirectional.html}
}

@ARTICLE{SAMPLING_BASED_1,
  author = {<NAME>. and <NAME>. and <NAME>.},
  title = {Path planning in expansive configuration spaces},
  journal = {Robotics and Automation, 1997. Proceedings., 1997 IEEE International
	Conference on},
  year = {1997},
  volume = {3},
  pages = {2719-2726 vol.3},
  month = {Apr},
  doi = {10.1109/ROBOT.1997.619371},
  keywords = {assembling, automobile industry, industrial manipulators, path planning,
	random processesassembly, automotive industry, expansive configuration
	spaces, path planning, random sampling, roadmap planner, robots}
}

@ARTICLE{POTENTIAL_FIELD_2,
  author = {<NAME> <NAME>.},
  title = {A potential field approach to path planning},
  journal = {Robotics and Automation, IEEE Transactions on},
  year = {1992},
  volume = {8},
  pages = {23-32},
  number = {1},
  month = {Feb},
  abstract = {A path-planning algorithm for the classical mover's problem in three
	dimensions using a potential field representation of obstacles is
	presented. A potential function similar to the electrostatic potential
	is assigned to each obstacle, and the topological structure of the
	free space is derived in the form of minimum potential valleys. Path
	planning is done at two levels. First, a global planner selects a
	robot's path from the minimum potential valleys and its orientations
	along the path that minimize a heuristic estimate of the path length
	and the chance of collision. Then, a local planner modifies the path
	and orientations to derive the final collision-free path and orientations.
	If the local planner fails, a new path and orientations are selected
	by the global planner and subsequently examined by the local planner.
	This process is continued until a solution is found or there are
	no paths left to be examined. The algorithm solves a much wider class
	of problems than other heuristic algorithms and at the same time
	runs much faster than exact algorithms (typically 5 to 30 min on
	a Sun 3/260)},
  doi = {10.1109/70.127236},
  issn = {1042-296X},
  keywords = {heuristic programming, planning (artificial intelligence), robots,
	topology5 to 30 min, Sun 3/260, collision-free orientations, collision-free
	path, global planner, local planner, minimum potential valleys, mover's
	problem, path length heuristic estimate minimization, path planning,
	potential field approach, potential field representation, robots,
	topological structure}
}

@ARTICLE{CONDENSATION,
  author = {<NAME> and <NAME>},
  title = {Condensation -- conditional density propagation for visual tracking},
  journal = {International Journal of Computer Vision},
  year = {1998},
  volume = {29},
  pages = {5-28},
  number = {1},
  url = {citeseer.ist.psu.edu/isard98condensation.html}
}

@ARTICLE{FOURIER_2,
  author = {<NAME>. and <NAME>.},
  title = {Image-based memory of environment},
  journal = {Proceedings of the 1996 IEEE/RSJ International Conference on Intelligent
	Robots and Systems},
  year = {1996},
  volume = {2},
  pages = {634-639 vol.2},
  doi = {10.1109/IROS.1996.571018},
  keywords = {Fourier transforms, intelligent control, mobile robots, navigation,
	robot visionFourier co-efficients, environment, environment geometry,
	iconic memory, image-based memory, intelligent agent, office, omnidirectional
	images}
}

@ARTICLE{REVIEW_HLAC_gesture,
  author = {<NAME> and <NAME>},
  title = {Gesture Recognition Using Auto-Regressive Coefficients of Higher-Order
	Local Auto-Correlation Features},
  journal = {Automatic Face and Gesture Recognition, IEEE International Conference
	on},
  year = {2004},
  volume = {0},
  pages = {583},
  address = {Los Alamitos, CA, USA},
  doi = {http://doi.ieeecomputersociety.org/10.1109/AFGR.2004.1301596},
  isbn = {0-7695-2122-3},
  publisher = {IEEE Computer Society}
}

@ARTICLE{09_jacobs95fast,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Fast Multiresolution Image Querying},
  journal = {Computer Graphics},
  year = {1995},
  volume = {29},
  pages = {277--286},
  number = {Annual Conference Series},
  url = {citeseer.ist.psu.edu/article/jacobs95fast.html}
}

@INPROCEEDINGS{JARVIS_ON_DISTANCE_TRANSFORM_BASED_COLLISION_FREE_PATH,
  author = {<NAME>},
  title = {On Distance Transform Based Collision-Free Path Planning for Robot
	Navigation in Known, Unknown and Time-Varying Environments},
  booktitle = {Advanced Mobile Robots},
  year = {1994},
  editor = {Prof. <NAME>},
  publisher = {World Scientific Publishing Co. Pty. Ltd.},
  owner = {nghia},
  timestamp = {2010.12.17}
}

@INPROCEEDINGS{DISTANCE_TRANSFORM,
  author = {<NAME>},
  title = {Distance transform based path planning for robot navigation},
  booktitle = {Recent Trends in Mobile Robots},
  year = {1993},
  editor = {<NAME>},
  volume = {2},
  publisher = {World Scientific},
  chapter = {1},
  owner = {nghia},
  timestamp = {2008.06.05}
}

@INPROCEEDINGS{JARVIS_TERRAIN_AWARE,
  author = {<NAME>},
  title = {Terrain-Aware Path Guided Robot Teleoperation in Virtual and real
	Space},
  booktitle = {ACHI},
  year = {2010},
  address = {St. Maartins},
  month = {Feb 10-14},
  owner = {nghia},
  timestamp = {2010.12.17}
}

@INPROCEEDINGS{JARVIS_COMPLEXITY_FLEXIBILITY,
  author = {<NAME>},
  title = {Robot Path Planning: Complexity, Flexibility and Application Scope},
  booktitle = {Practical Cognitive Agents and Robots},
  year = {2007},
  pages = {3-14},
  address = {Perth, Australia},
  month = {Nov 27-28},
  owner = {nghia},
  timestamp = {2010.12.17}
}

@INPROCEEDINGS{DISTANCE_TRANSFORM_2,
  author = {<NAME>},
  title = {Robot path planning: complexity, flexibility and application scope},
  booktitle = {PCAR '06: Proceedings of the 2006 international symposium on Practical
	cognitive agents and robots},
  year = {2006},
  pages = {3--14},
  address = {New York, NY, USA},
  publisher = {ACM},
  doi = {http://doi.acm.org/10.1145/1232425.1232430},
  isbn = {1-74052-130-7},
  location = {Perth, Australia}
}

@INPROCEEDINGS{DISTANCE_TRANSFORM_COLLISION_FREE,
  author = {<NAME>},
  title = {Collision-Free Trajectory Planning Using Distance Transforms},
  booktitle = {National Conference and Exhibition on Robotics},
  year = {1984},
  address = {Melbourne, Australia},
  month = {Aug 20-24},
  owner = {nghia},
  timestamp = {2010.12.17}
}

@INPROCEEDINGS{JARVIS_GROWING_POLYHEDRAL,
  author = {<NAME>},
  title = {Growing Polygedral Obstacles for Planning Collision-Free Paths},
  booktitle = {The Australian Computer Journal},
  year = {1983},
  volume = {15},
  series = {3},
  pages = {103-111},
  month = {Aug},
  owner = {nghia},
  timestamp = {2010.12.17}
}

@ARTICLE{01_Jarvis_Byrne_86,
  author = {<NAME> and <NAME>},
  title = {Robot Navigation : Touching, Seeing and Knowing},
  journal = {1st Australian Artificial Intelligence Congress, Melbourne},
  year = {1986},
  pages = {Section e. (12 pages)},
  month = {18-20 Nov}
}

@INPROCEEDINGS{CYBERWORLD,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Autonomous Robot Navigation in Cyber and RealWorlds},
  booktitle = {CW '07: Proceedings of the 2007 International Conference on Cyberworlds},
  year = {2007},
  pages = {66--73},
  address = {Washington, DC, USA},
  publisher = {IEEE Computer Society},
  doi = {http://dx.doi.org/10.1109/CW.2007.20},
  isbn = {0-7695-3005-2}
}

@INPROCEEDINGS{MHT_1,
  author = {<NAME> and <NAME>},
  title = {Active global localisation for a mobile robot using multiple hypothesis
	tracking},
  booktitle = {IEEE Transactions on Robotics and Automation},
  year = {1999},
  pages = {13-22},
  text = {<NAME> and <NAME>. Active global localisation for a mobile
	robot using multiple hypothesis tracking. In Proc. of the IJCAI-99
	Workshop on Reasoning with Uncertainty in Robot Navigation, 1999.},
  url = {citeseer.ist.psu.edu/jensfelt99active.html}
}

@ARTICLE{IMU_KALMAN,
  author = {<NAME>. and <NAME>. and <NAME>. and <NAME>. and
	<NAME>.},
  title = {A method for dead reckoning parameter correction in pedestrian navigation
	system},
  journal = {Instrumentation and Measurement, IEEE Transactions on},
  year = {2003},
  volume = {52},
  pages = { 209-215},
  number = {1},
  month = {Feb},
  abstract = { This paper presents a method for correcting dead reckoning parameters,
	which are heading and step size, for a pedestrian navigation system.
	In this method, the compass bias error and the step size error can
	be estimated during the period that the Global Positioning System
	(GPS) signal is available. The errors are used for correcting those
	parameters to improve the accuracy of position determination using
	only the dead reckoning system when the GPS signal is not available.
	The results show that the parameters can be estimated with reasonable
	accuracy. Moreover, the method also helps to increase the positioning
	accuracy when the GPS signal is available.},
  doi = {10.1109/TIM.2002.807986},
  issn = {0018-9456},
  keywords = { Global Positioning System, Kalman filters, error correction, measurement
	errors, measurement theory GPS signal, Global Positioning System,
	Kalman filter, compass bias error estimation, dead reckoning parameter
	correction, heading, measurement model, pedestrian navigation system,
	positioning accuracy, step size error estimation}
}

@MISC{902882,
  author = {<NAME> and <NAME>},
  title = {Robust localization using panoramic view-based recognition},
  year = {2000},
  doi = {10.1109/ICPR.2000.902882},
  journal = {Pattern Recognition, 2000. Proceedings. 15th International Conference
	on},
  keywords = {image recognition, mobile robots, path planning, robot visioncluttered
	environments, panoramic view-based recognition, robust localization,
	spatial localization, view-based localization},
  pages = {136-139 vol.4},
  volume = {4}
}

@INPROCEEDINGS{EIGENSPACE_1,
  author = {<NAME> and <NAME>onardis},
  title = {Panoramic Eigenimages for Spatial Localisation},
  booktitle = {Computer Analysis of Images and Patterns},
  year = {1999},
  pages = {558-567},
  url = {citeseer.ist.psu.edu/jogan99panoramic.html}
}

@INPROCEEDINGS{UKF,
  author = {<NAME> and <NAME>},
  title = {A new extension of the Kalman filter to nonlinear systems},
  booktitle = {Int. Symp. Aerospace/Defense Sensing, Simul. and Controls},
  year = {1997},
  pages = {182--193}
}

@ARTICLE{USER_LOCALIZATION_FIDUCIAL_2,
  author = {<NAME>. and <NAME> <NAME> <NAME>. and Kaufmann,
	H. and <NAME>.},
  title = {Structured visual markers for indoor pathfinding},
  journal = {Augmented Reality Toolkit, The First IEEE International Workshop},
  year = {2002},
  pages = { 8 pp.-},
  abstract = { We present a mobile augmented reality (AR) system to guide a user
	through an unfamiliar building to a destination room. The system
	presents a world-registered wireframe model of the building labeled
	with directional information in a see-through heads-up display, and
	a three-dimensional world-in-miniature (WIM) map on a wrist-worn
	pad that also acts as an input device. Tracking is done using a combination
	of wall-mounted ARToolkit markers observed by a head-mounted camera,
	and an inertial tracker. To allow coverage of arbitrarily large areas
	with a limited set of markers, a structured marker re-use scheme
	based on graph coloring has been developed.},
  doi = {10.1109/ART.2002.1107018},
  issn = { },
  keywords = { augmented reality, optical tracking, position control ARToolkit,
	graph coloring, head-mounted camera, inertial tracker, mobile augmented
	reality system, see-through heads-up display, world-in-miniature
	map, world-registered wireframe model}
}

@ARTICLE{KALMAN,
  author = {Kalman, <NAME>},
  title = {A New Approach to Linear Filtering and Prediction Problems},
  journal = {Transactions of the ASME--Journal of Basic Engineering},
  year = {1960},
  volume = {82},
  pages = {35--45},
  number = {Series D}
}

@INPROCEEDINGS{ADAPTIVE_PARTICLE_Koller98,
  author = {<NAME> and <NAME>},
  title = {Using Learning for Approximation in Stochastic Processes},
  booktitle = {In Proceedings of the International Conference on Machine Learning
	(ICML},
  year = {1998},
  pages = {287--295}
}

@ARTICLE{POTENTIAL_FIELD_1,
  author = {<NAME>. and <NAME>.},
  title = {Potential field methods and their inherent limitations for mobile
	robot navigation},
  journal = {Robotics and Automation, 1991. Proceedings., 1991 IEEE International
	Conference on},
  year = {1991},
  pages = {1398-1404 vol.2},
  month = {Apr},
  doi = {10.1109/ROBOT.1991.131810},
  keywords = {computerised navigation, mobile robots, position controlcomputerised
	navigation, mobile robot, obstacle avoidance, potential field methods,
	sampling time, vector field histogram}
}

@ARTICLE{REVIEW_FINALE,
  author = {<NAME> and Kak, <NAME>.},
  title = {Fast vision-guided mobile robot navigation using model-based reasoning
	and prediction of uncertainties},
  journal = {CVGIP: Image Underst.},
  year = {1992},
  volume = {56},
  pages = {271--329},
  number = {3},
  address = {Orlando, FL, USA},
  doi = {http://dx.doi.org/10.1016/1049-9660(92)90045-5},
  issn = {1049-9660},
  publisher = {Academic Press, Inc.}
}

@ARTICLE{VERTICAL_LINES_2,
  author = {<NAME> and <NAME>},
  title = {Fast vision-guided mobile robot navigation using model-based reasoning
	and prediction of uncertainties},
  journal = {CVGIP: Image Underst.},
  year = {1992},
  volume = {56},
  pages = {271--329},
  number = {3},
  address = {Orlando, FL, USA},
  doi = {http://dx.doi.org/10.1016/1049-9660(92)90045-5},
  issn = {1049-9660},
  publisher = {Academic Press, Inc.}
}

@ARTICLE{MHL_2,
  author = {<NAME>. and <NAME>.},
  title = {An experimental comparison of localisation methods, the MHL sessions},
  journal = {Intelligent Robots and Systems, 2003. (IROS 2003). Proceedings. 2003
	IEEE/RSJ International Conference on},
  year = {2003},
  volume = {1},
  pages = { 992-997 vol.1},
  month = {Oct.},
  abstract = { In this paper we compare multi hypothesis localisation (MHL)-which
	is a mobile robot localisation method based on multi hypothesis tracking
	- with six other methods reported in the literature. The comparison
	is performed using a standard set of test data and corresponding
	evaluation tools, thus facilitating a direct comparison of the obtained
	results. The experiments show that MHL compares favourably to all
	other methods in terms of recovering when the robot has been kidnapped.
	When using a validation gate for filtering out noisy measurements,
	MHL and the standard extended Kalman filter both perform as well
	as all other reported methods in terms of accuracy while being faster
	to compute.},
  doi = {10.1109/IROS.2003.1250757},
  issn = { },
  keywords = { Kalman filters, filtering theory, interference suppression, mobile
	robots Kalman filter, mobile robot localisation method, multihypothesis
	localisation, multihypothesis tracking, noise filtering}
}

@INPROCEEDINGS{REVIEW_PCA,
  author = {<NAME>. and <NAME>.},
  title = {Probabilistic localization by appearance models and active vision
	},
  booktitle = {Robotics and Automation, 1999. Proceedings. 1999 IEEE International
	Conference on},
  year = {1999},
  volume = {3},
  pages = {2255-2260 vol.3},
  abstract = {In order to do useful things a mobile robot needs some sort of global
	information about the environment it is operating in. In the paper
	an approach is described where the global information is not cast
	in a model of the geometry of the environment but in a model of all
	sensory data of the robot. As a primary sensing system we use computer
	vision. The model gives a probability distribution over the learned
	locations given an observation. We developed an active vision strategy
	to increase the performance and tested the method on real image data
	from our robot},
  doi = {10.1109/ROBOT.1999.770441},
  keywords = {active vision, mobile robots, path planning, probability, robot visionappearance
	models, computer vision, global information, learned locations, primary
	sensing system, probabilistic localization, probability distribution,
	sensory data}
}

@INPROCEEDINGS{REVIEW_Krotkov_1989_3656,
  author = {<NAME>},
  title = {Mobile robot localization using a single image},
  booktitle = {Proceedings of the IEEE International Conference on Robotics and
	Automation (ICRA '89)},
  year = {1989},
  volume = {2},
  pages = {978 - 983},
  month = {May}
}

@BOOK{LATOMBE_91,
  title = {Robot Motion Planning},
  publisher = {Kluwer Academic Publishers},
  year = {1991},
  author = {<NAME>},
  owner = {nghia},
  timestamp = {2008.10.27}
}

@INPROCEEDINGS{LAVALLE_RRT,
  author = {<NAME>},
  title = {Rapidly-exploring random trees: A new tool for path planning},
  booktitle = {Tech report},
  year = {1998},
  address = {Dept. Of Computer Science, Iowa State University},
  owner = {nghia},
  timestamp = {2010.12.17}
}

@INPROCEEDINGS{RRT,
  author = {<NAME> and <NAME>},
  title = {Randomized kinodynamic planning},
  booktitle = {Proc. {IEEE} Int'l Conf. on Robotics and Automation},
  year = {1999},
  note = {To appear.},
  url = {citeseer.ist.psu.edu/article/lavalle99randomized.html}
}

@ARTICLE{SIFT_3,
  author = {<NAME> and <NAME>},
  title = {Reduced SIFT Features For Image Retrieval and Indoor Localisation
	},
  journal = {Australasian Conference on Robotics and Automation},
  year = {2004}
}

@BOOK{WHYTE_SONAR,
  title = {Directed Sonar Sensing for Mobile Robot Navigation},
  publisher = {Kluwer Academic Publishers},
  year = {1992},
  author = {<NAME> and <NAME>},
  address = {Norwell, MA, USA},
  isbn = {0792392426}
}

@ARTICLE{SLAM_IROS_91,
  author = {<NAME>. and <NAME>. },
  title = {Simultaneous map building and localization for an autonomous mobile
	robot},
  journal = {Intelligent Robots and Systems '91. 'Intelligence for Mechanical
	Systems, Proceedings IROS '91. IEEE/RSJ International Workshop on},
  year = {1991},
  pages = {1442--1447 vol.3}
}

@INPROCEEDINGS{SLAM_2,
  author = {<NAME> and <NAME> and <NAME>},
  title = {A computationally efficient method for large-scale concurrent mapping
	and localization},
  booktitle = {Proceedings of the Ninth International Symposium on Robotics Research},
  year = {1999},
  pages = {169--176},
  publisher = {Springer-Verlag}
}

@INPROCEEDINGS{LINGERMANN,
  author = {<NAME>. and <NAME>. and <NAME>. and <NAME>.},
  title = {Indoor and outdoor localization for fast mobile robots},
  booktitle = {Intelligent Robots and Systems},
  year = {2004},
  volume = {3},
  pages = { 2185-2190 vol.3},
  month = {Sept.-2 Oct.},
  abstract = { This paper describes a novel, laser-based approach for tracking the
	pose of a high-speed mobile robot. The algorithm is outstanding in
	terms of accuracy and computational time, being 33 times faster than
	real time. The efficiency is achieved by a closed form solution for
	the matching of two lasers scans, the use of natural landmarks and
	fast linear filters. The implemented algorithm is evaluated with
	the high-speed robot Kurt3D (4 m/s), and compared to standard scan
	matching methods in indoor and outdoor environments.},
  doi = {10.1109/IROS.2004.1389733},
  journal = {Intelligent Robots and Systems, 2004. (IROS 2004). Proceedings. 2004
	IEEE/RSJ International Conference on},
  keywords = { mobile robots, optical scanners, optical tracking, path planning,
	position control Kurt3D, indoor localization, laser tracking, lasers
	scan, linear filter, mobile robot, natural landmark, outdoor localization}
}

@ARTICLE{Linåker2006205,
  author = {<NAME> and <NAME>},
  title = {Real-time appearance-based Monte Carlo localization},
  journal = {Robotics and Autonomous Systems},
  year = {2006},
  volume = {54},
  pages = {205 - 220},
  number = {3},
  doi = {DOI: 10.1016/j.robot.2005.11.003},
  issn = {0921-8890},
  keywords = {Appearance-based localization},
  url = {http://www.sciencedirect.com/science/article/B6V16-4HVW8BD-1/2/03a80badd43858727353803875f5bcbf}
}

@ARTICLE{REVIEW_HLAC_Linåker2006205,
  author = {<NAME> and <NAME>},
  title = {Real-time appearance-based Monte Carlo localization},
  journal = {Robotics and Autonomous Systems},
  year = {2006},
  volume = {54},
  pages = {205 - 220},
  number = {3},
  doi = {DOI: 10.1016/j.robot.2005.11.003},
  issn = {0921-8890},
  keywords = {Appearance-based localization},
  url = {http://www.sciencedirect.com/science/article/B6V16-4HVW8BD-1/2/03a80badd43858727353803875f5bcbf}
}

@ARTICLE{PCA_VARIANTS,
  author = {<NAME> and <NAME>},
  title = {Variants of Principal Components Analysis},
  journal = {Geoscience and Remote Sensing Symposium, 2007. IGARSS 2007. IEEE
	International},
  year = {2007},
  pages = {1083-1086},
  month = {July},
  doi = {10.1109/IGARSS.2007.4422989},
  keywords = {covariance matrices, geophysical techniques, principal component analysisPGPCA,
	PRPCA, SCPCA, SMPCA, covariance matrix, principal components analysis
	variants, prioritized PCA, progressive PCA, simultaneous PCA, successive
	PCA}
}

@INPROCEEDINGS{ICP_COMPARISON,
  author = {<NAME> and <NAME> and <NAME>},
  title = {A comparison of four algorithms for estimating 3-D rigid transformations},
  booktitle = {BMVC '95: Proceedings of the 1995 British conference on Machine vision
	(Vol. 1)},
  year = {1995},
  pages = {237--246},
  address = {Surrey, UK, UK},
  publisher = {BMVA Press},
  isbn = {0-9521898-2-8},
  location = {Birmingham, United Kingdom}
}

@ARTICLE{SIFT,
  author = {<NAME>. },
  title = {Distinctive Image Features from Scale-Invariant Keypoints},
  journal = {International Journal of Computer Vision},
  year = {2004},
  volume = {60},
  pages = {91--110},
  number = {2},
  month = {November},
  citeulike-article-id = {300557},
  keywords = {features},
  url = {http://www.metapress.com/link.asp?id=H4L02691327PX768}
}

@INPROCEEDINGS{LOAZANO_ALGORITHM,
  author = {<NAME> and <NAME>},
  title = {An Algorithm for Planning Collision-free Paths Among Polyhedral Obstacles},
  booktitle = {Communications of the ACM},
  year = {1979},
  volume = {22},
  series = {10},
  pages = {560-570},
  month = {Oct},
  owner = {nghia},
  timestamp = {2010.12.17}
}

@ARTICLE{LU_MILIOS,
  author = {<NAME> and <NAME>},
  title = {Robot Pose Estimation in Unknown Environments by Matching 2D Range
	Scans},
  journal = {J. Intell. Robotics Syst.},
  year = {1997},
  volume = {18},
  pages = {249--275},
  number = {3},
  address = {Hingham, MA, USA},
  doi = {http://dx.doi.org/10.1023/A:1007957421070},
  issn = {0921-0296},
  publisher = {Kluwer Academic Publishers}
}

@INPROCEEDINGS{Lucas81aniterative,
  author = {<NAME> and <NAME>},
  title = {An Iterative Image Registration Technique with an Application to
	Stereo Vision (DARPA)},
  booktitle = {Proceedings of the 1981 DARPA Image Understanding Workshop},
  year = {1981},
  pages = {121-130},
  month = {April}
}

@ARTICLE{USER_LOCALIZATION_WIRELESS,
  author = {<NAME>. and Ogst Chen and <NAME>.},
  title = {Mobile user localization in wireless sensor network using grey prediction
	method},
  journal = {Industrial Electronics Society, 2005. IECON 2005. 31st Annual Conference
	of IEEE},
  year = {2005},
  pages = { 6 pp.-},
  month = {Nov.},
  abstract = { Knowing the position of mobile user is an important role for location
	services in the building. The characteristics of wireless sensor
	network are low power, low cost and low complexity. With these functions,
	wireless sensor network have great potential to develop indoor position
	system. However, radio signal propagation is easily affected by diffraction,
	reflections, and scattering of radio in the building, the received
	signal strength need good calibration method to improve the accuracy
	of position estimation system. In this paper we use grey prediction
	method in wireless sensor network and employ wireless LAN medium
	(Zigbee/802.15.4). The grey prediction is used to predict the tendency
	of RSSI (received signal strength indicator), and we also designed
	dynamic triangular (DTN) location method. We have done some experiments
	and compare with other classical location finding methods. The mean
	distant error of RSSI on mobile user can be within 2.3 m at offline
	stage. As a result, grey predication with DTN provides more accurate
	predicted position and carries out mean distance error within 1.3
	m at run-time stage.},
  doi = {10.1109/IECON.2005.1569330},
  keywords = { mobile radio, prediction theory, radiowave propagation, wireless
	LAN, wireless sensor networks RSSI, calibration method, dynamic triangular
	location method, grey prediction method, mobile user, position estimation
	system, radio signal propagation, received signal strength indicator,
	wireless LAN medium, wireless sensor network}
}

@CONFERENCE{FERN_1,
  author = {<NAME>, <NAME> and <NAME>},
  title = {Fast Keypoint Recognition in Ten Lines of Code},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year = {2007},
  owner = {nghia},
  timestamp = {2012.01.28}
}

@INPROCEEDINGS{3D_maciel95visual,
  author = {<NAME> and <NAME>},
  title = {Visual Navigation of Large Environments Using Textured Clusters},
  booktitle = {Symposium on Interactive 3D Graphics},
  year = {1995},
  pages = {95-102, 211},
  url = {citeseer.ist.psu.edu/maciel95visual.html}
}

@ARTICLE{MARS_ROVER,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Two years of Visual Odometry on the Mars Exploration Rovers: Field
	Reports},
  journal = {J. Field Robot.},
  year = {2007},
  volume = {24},
  pages = {169--186},
  number = {3},
  address = {Chichester, UK, UK},
  doi = {http://dx.doi.org/10.1002/rob.v24:3},
  issn = {1556-4959},
  publisher = {John Wiley and Sons Ltd.}
}

@INPROCEEDINGS{PIANO_MOVER,
  author = {<NAME>\"{e}l},
  title = {The algorithm by Schwartz, Sharir and Collins on the piano mover's
	problem},
  booktitle = {Proceedings of the Workshop on Geometry and Robotics},
  year = {1989},
  pages = {49--66},
  address = {London, UK},
  publisher = {Springer-Verlag},
  isbn = {3-540-51683-2}
}

@INPROCEEDINGS{JAARVIS_MARZOUQI_2,
  author = {<NAME> and <NAME>},
  title = {Distance Transform Based Gaussian Distribution for Probabilistic
	Target Tracking},
  booktitle = {International Conference on Intelligent Robots and System},
  year = {2006},
  address = {Beijing, China},
  month = {Oct 9-15},
  owner = {nghia},
  timestamp = {2010.12.17}
}

@INPROCEEDINGS{JARVIS_COVERT_ROBOTICS,
  author = {<NAME> and <NAME>},
  title = {Covert Robotics: Covert Path Planning in Unknown Environments},
  booktitle = {Australasian Conference on Robotics and Automation},
  year = {2004},
  pages = {8},
  address = {Canberra, Australia},
  month = {Dec 6-8},
  owner = {nghia},
  timestamp = {2010.12.17}
}

@INPROCEEDINGS{FOURIER_1,
  author = {<NAME> and <NAME>},
  title = {Image-based monte-carlo localisation with omnidirectional images},
  booktitle = {Conference of the Italian Association of Artificial Intelligence},
  year = {2004}
}

@INPROCEEDINGS{SLAM_Montemerlo03a,
  author = {<NAME> and <NAME> and <NAME> and <NAME>},
  title = {{FastSLAM} 2.0: An Improved Particle Filtering Algorithm for Simultaneous
	Localization and Mapping that Provably Converges},
  booktitle = {Proceedings of the Sixteenth International Joint Conference on Artificial
	Intelligence (IJCAI)},
  year = {2003},
  address = {Acapulco, Mexico},
  organization = {IJCAI}
}

@PHDTHESIS{Moravec_1980_22,
  author = {<NAME>},
  title = {Obstacle Avoidance and Navigation in the Real World by a Seeing Robot
	Rover},
  school = {Stanford University},
  year = {1980},
  address = {Stanford, CA, USA}
}

@INPROCEEDINGS{SURF_LOCALIZATION,
  author = {<NAME>. and Guerrero, J.J. and <NAME>.},
  title = {SURF features for efficient robot localization with omnidirectional
	images},
  booktitle = {Robotics and Automation, 2007 IEEE International Conference on},
  year = {2007},
  pages = {3901-3907},
  month = {April},
  abstract = {Many robotic applications work with visual reference maps, which usually
	consist of sets of more or less organized images. In these applications,
	there is a compromise between the density of reference data stored
	and the capacity to identify later the robot localization, when it
	is not exactly in the same position as one of the reference views.
	Here we propose the use of a recently developed feature, SURF, to
	improve the performance of appearance-based localization methods
	that perform image retrieval in large data sets. This feature is
	integrated with a vision-based algorithm that allows both topological
	and metric localization using omnidirectional images in a hierarchical
	approach. It uses pyramidal kernels for the topological localization
	and three-view geometric constraints for the metric one. Experiments
	with several omnidirectional images sets are shown, including comparisons
	with other typically used features (radial lines and SIFT). The advantages
	of this approach are proved, showing the use of SURF as the best
	compromise between efficiency and accuracy in the results.},
  doi = {10.1109/ROBOT.2007.364077},
  issn = {1050-4729},
  keywords = {SLAM (robots), feature extraction, image matching, image retrieval,
	robot visionSURF features, appearance-based localization, image retrieval,
	metric localization, omnidirectional images, radial lines, robot
	localization, topological localization, vision-based algorithm, visual
	reference maps}
}

@BOOK{INTRO_TO_AI_ROBOTICS,
  title = {Introduction to AI Robotics},
  publisher = {MIT Press},
  year = {2000},
  author = {<NAME>},
  address = {Cambridge, MA, USA},
  isbn = {0262133830}
}

@INPROCEEDINGS{REVIEW_HLAC,
  author = {<NAME>, <NAME>},
  title = {A new scheme for flexible and intelligent vision systems},
  booktitle = {IAPR Workshop on Computer Vision},
  year = {1988},
  owner = {nghia},
  timestamp = {2009.07.21}
}

@ARTICLE{USER_LOCALIZATION_FIDUCIAL,
  author = {<NAME>. and <NAME>.},
  title = {Circular data matrix fiducial system and robust image processing
	for a wearable vision-inertial self-tracker},
  journal = {Mixed and Augmented Reality, 2002. ISMAR 2002. Proceedings. International
	Symposium on},
  year = {2002},
  pages = { 27-36},
  abstract = { A wearable low-power hybrid vision-inertial tracker has been demonstrated
	based on a flexible sensor fusion core architecture, which allows
	easy reconfiguration by plugging-in different kinds of sensors. A
	particular prototype implementation consists of one inertial measurement
	unit and one out-ward-looking wide-angle smart camera, with a built-in
	DSP to run all required image-processing tasks. The smart camera
	operates on newly designed 2D bar-coded fiducials printed on a standard
	black-and-white printer. The fiducial design allows having thousands
	of different codes, thus enabling uninterrupted tracking throughout
	a large building or even a campus at very reasonable cost. The system
	operates in various real-world lighting conditions without user intervention
	due to homomorphic image processing algorithms for extracting fiducials
	in the presence of very non-uniform lighting.},
  doi = {10.1109/ISMAR.2002.1115065},
  issn = { },
  keywords = { augmented reality, computer vision, optical tracking, sensor fusion
	2D bar-coded fiducials, black-and-white printer, built-in DSP, campus,
	circular data matrix fiducial system, flexible sensor fusion core
	architecture, homomorphic image processing algorithms, inertial measurement
	unit, large building, nonuniform lighting, outward-looking wide-angle
	smart camera, real-world lighting conditions, reconfiguration, robust
	image processing, uninterrupted tracking, wearable low-power hybrid
	vision-inertial self-tracker}
}

@ARTICLE{USER_LOCALIZATION_IR_LED,
  author = {<NAME>. and <NAME> <NAME>.},
  title = {Discreet markers for user localization},
  journal = {Wearable Computers, 2004. ISWC 2004. Eighth International Symposium
	on},
  year = {2004},
  volume = {1},
  pages = { 172-173},
  month = {Oct.-3 Nov.},
  abstract = { This paper describes a localization method for wearable computer
	users. To realize applications of wearable computers like a navigation
	system, the position of a user is required for location-based services.
	Many localization methods in indoor environments have been proposed.
	One of the methods estimates user's position using IR beacons or
	visual markers. However, these methods have problems of power supply
	and/or undesirable visual effects. In order to avoid the problems,
	we propose a new localization method which is based on using an IR
	camera and discreet markers consisting of translucent retro-reflectors.
	In the proposed method, to extract the regions of the markers from
	the captured images stably, the camera captures the reflection of
	IR LEDs which are flashed on and off synchronously.},
  doi = {10.1109/ISWC.2004.15},
  issn = {1530-0811 },
  keywords = { mobile computing, mobility management (mobile radio), user interfaces
	IR camera, discreet markers, location-based services, navigation
	system, user localization, wearable computer users}
}

@MISC{PATHFINDER,
  author = {NASA},
  title = {{JPL Mars Pathfinder}},
  year = {1996},
  note = {http://mpfwww.jpl.nasa.gov/MPF/index1.html},
  owner = {nghia},
  timestamp = {2008.12.17}
}

@ARTICLE{LOOP_CLOSING,
  author = { <NAME>. and <NAME>},
  title = {SLAM-Loop Closing with Visually Salient Features},
  journal = {Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005
	IEEE International Conference on},
  year = {2005},
  pages = { 635-642},
  month = {April},
  abstract = { Within the context of Simultaneous Localisation and Mapping (SLAM),
	“loop closing” is the task of deciding whether or not a vehicle has,
	after an excursion of arbitrary length, returned to a previously
	visited area. Reliable loop closing is both essential and hard. It
	is without doubt one of the greatest impediments to long term, robust
	SLAM. This paper illustrates how visual features, used in conjunction
	with scanning laser data, can be used to a great advantage. We use
	the notion of visual saliency to focus the selection of suitable
	(affine invariant) image-feature descriptors for storage in a database.
	When queried with a recently taken image the database returns the
	capture time of matching images. This time information is used to
	discover loop closing events. Crucially this is achieved independently
	of estimated map and vehicle location. We integrate the above technique
	into a SLAM algorithm using delayed vehicle states and scan matching
	to form interpose geometric constraints. We present initial results
	using this system to close loops (around 100m) in an indoor environment.
	},
  keywords = {null Loop Closing , Mobile Robotics , SLAM , Saliency , Visual Features}
}

@BOOK{NILSSON_PROBLEM_SOLVING,
  title = {Problem-Solving Methods in Artificial Intelligence},
  publisher = {McGraw-Hill},
  year = {1979},
  author = {<NAME>},
  owner = {nghia},
  timestamp = {2010.12.17}
}

@ARTICLE{Nister06visualodometry,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Visual odometry for ground vehicle applications},
  journal = {Journal of Field Robotics},
  year = {2006},
  volume = {23},
  pages = {2006}
}

@ARTICLE{NISTER2,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Visual Odometry},
  journal = {Computer Vision and Pattern Recognition, IEEE Computer Society Conference
	on},
  year = {2004},
  volume = {1},
  pages = {652-659},
  address = {Los Alamitos, CA, USA},
  doi = {http://doi.ieeecomputersociety.org/10.1109/CVPR.2004.265},
  issn = {1063-6919},
  publisher = {IEEE Computer Society}
}

@ARTICLE{REVIEW_VISION_SONAR,
  author = {<NAME>. and <NAME>. and <NAME>.},
  title = {Vision-based navigation by a mobile robot with obstacle avoidance
	using single-camera vision and ultrasonic sensing},
  journal = {Robotics and Automation, IEEE Transactions on},
  year = {1998},
  volume = {14},
  pages = {969-978},
  number = {6},
  month = {Dec},
  doi = {10.1109/70.736780},
  issn = {1042-296X},
  keywords = {collision avoidance, mobile robots, navigation, robot vision, self-adjusting
	systems, ultrasonic transducersYAMABICO robot, mobile robot, obstacle
	avoidance, position correction system, robot vision, self-localization,
	single-camera vision, ultrasonic sensing, vision-based navigation}
}

@INPROCEEDINGS{ZPR,
  author = {Tom\'{a}s Pajdla and V\'{a}<NAME>\'{a}c},
  title = {Zero Phase Representation of Panoramic Images for Image Vased Localization},
  booktitle = {CAIP '99: Proceedings of the 8th International Conference on Computer
	Analysis of Images and Patterns},
  year = {1999},
  pages = {550--557},
  address = {London, UK},
  publisher = {Springer-Verlag},
  isbn = {3-540-66366-5}
}

@INPROCEEDINGS{EIGENSPACE_2,
  author = {<NAME> and <NAME> and <NAME>.},
  title = {Robust localization using context in omnidirectional imaging},
  booktitle = {Proceding of the International Conference on Robotics \& Automation},
  year = {2001}
}

@INPROCEEDINGS{SIFT_7,
  author = {<NAME>, <NAME>},
  title = {σMCL: Monte-Carlo Localization for Mobile Robots with Stereo Vision},
  booktitle = {Robotics: Science and Systems I},
  year = {2005},
  owner = {nghia},
  timestamp = {2009.07.21}
}

@ARTICLE{PARTICLE_FILTER,
  author = {<NAME> and <NAME>},
  title = {Filtering via Simulation: Auxiliary Particle Filters},
  journal = {Journal of the American Statistical Association},
  year = {1999},
  volume = {94},
  pages = {590--??},
  number = {446},
  url = {citeseer.ist.psu.edu/pitt97filtering.html}
}

@INPROCEEDINGS{WAVELETS_AIBO,
  author = {<NAME> and <NAME> and <NAME> and Yoshiaki
	Jitsukawa and <NAME> and <NAME>},
  title = {Toward Image-Based Localization for AIBO Using Wavelet Transform.},
  booktitle = {AI*IA},
  year = {2007},
  editor = {<NAME> and <NAME>},
  volume = {4733},
  series = {Lecture Notes in Computer Science},
  pages = {831-838},
  publisher = {Springer},
  date = {2007-08-27},
  description = {dblp},
  ee = {http://dx.doi.org/10.1007/978-3-540-74782-6_75},
  isbn = {978-3-540-74781-9},
  keywords = {dblp },
  url = {http://dblp.uni-trier.de/db/conf/aiia/aiia2007.html#PrettoMPJUA07}
}

@ARTICLE{IMU_ASSESSMENT,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Assessment of Step Determination in a GPS/Compass/IMU System for
	Personal Positioning},
  journal = {Proceedings of ION GNSS 2007},
  year = {2007},
  owner = {nghia},
  timestamp = {2008.11.24}
}

@ARTICLE{MHT,
  author = { <NAME>.},
  title = {An algorithm for tracking multiple targets},
  journal = {Automatic Control, IEEE Transactions on},
  year = {1979},
  volume = {24},
  pages = { 843-854},
  number = {6},
  month = {Dec},
  abstract = { An algorithm for tracking multiple targets in a cluttered enviroment
	is developed. The algorithm is capable of initiating tracks, accounting
	for false or missing reports, and processing sets of dependent reports.
	As each measurement is received, probabilities are calculated for
	the hypotheses that the measurement came from previously known targets
	in a target file, or from a new target, or that the measurement is
	false. Target states are estimated from each such data-association
	hypothesis using a Kalman filter. As more measurements are received,
	the probabilities of joint hypotheses are calculated recursively
	using all available information such as density of unknown targets,
	density of false targets, probability of detection, and location
	uncertainty. This branching technique allows correlation of a measurement
	with its source based on subsequent, as well as previous, data. To
	keep the number of hypotheses reasonable, unlikely hypotheses are
	eliminated and hypotheses with similar target estimates are combined.
	To minimize computational requirements, the entire set of targets
	and measurements is divided into clusters that are solved independently.
	In an illustrative example of aircraft tracking, the algorithm successfully
	tracks targets over a wide range of conditions.},
  issn = {0018-9286},
  keywords = {null Bayes procedures, Tracking}
}

@TECHREPORT{PARTICLE_TUTORIAL,
  author = {<NAME>},
  title = {A Particle Filter Tutorial for Mobile Robot Localization},
  institution = {Centre for Intelligent Machines, McGill University},
  year = {2004},
  number = {TR-CIM-04-02},
  address = {3480 University St., Montreal, Qu\'ebec, CANADA H3A 2A7}
}

@INPROCEEDINGS{ROSENFELD_SEQUENTIAL,
  author = {<NAME> and <NAME>},
  title = {Sequential Operations in Digital Image Processing},
  booktitle = {J.A.C.M.},
  year = {1966},
  volume = {13},
  series = {4},
  pages = {471-494},
  owner = {nghia},
  timestamp = {2010.12.17}
}

@INPROCEEDINGS{rosten_2006_machine,
  author = {<NAME> and <NAME>},
  title = {Machine learning for high-speed corner detection},
  booktitle = {European Conference on Computer Vision},
  year = {2006},
  volume = {1},
  pages = {430--443},
  month = {May},
  doi = {10.1007/11744023_34},
  notes = {Poster presentation},
  url = {http://edwardrosten.com/work/rosten_2006_machine.pdf}
}

@ARTICLE{ICP_VARIANT,
  author = {<NAME> and <NAME>},
  title = {Efficient Variants of the ICP Algorithm},
  journal = {3dim},
  year = {2001},
  volume = {0},
  pages = {145},
  address = {Los Alamitos, CA, USA},
  doi = {http://doi.ieeecomputersociety.org/10.1109/IM.2001.924423},
  isbn = {0-7695-0984-3},
  publisher = {IEEE Computer Society}
}

@INPROCEEDINGS{DISABLED_PATIENTS,
  author = {<NAME>. and <NAME>. and <NAME>.},
  title = {Portable monitoring and localization systems for disabled patients},
  booktitle = {IEVM06},
  year = {2006},
  bibsource = {http://www.visionbib.com/bibliography/motion-f740.html#TT57461}
}

@ARTICLE{schmid00evaluation,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Evaluation of Interest Point Detectors},
  journal = {International Journal of Computer Vision},
  year = {2000},
  volume = {37},
  pages = {151-172},
  number = {2},
  url = {citeseer.ist.psu.edu/schmid00evaluation.html}
}

@ARTICLE{RPP,
  author = {<NAME>. and <NAME>.},
  title = {Robust Pose Estimation from a Planar Target},
  journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
  year = {2006},
  volume = {28},
  pages = {2024 -2030},
  number = {12},
  month = {dec. },
  doi = {10.1109/TPAMI.2006.252},
  issn = {0162-8828},
  keywords = {calibrated camera;camera pose ambiguity;camera pose tracking;noncollinear
	points;planar target;pose estimation algorithms;robust pose estimation;calibration;cameras;pose
	estimation;target tracking;Algorithms;Artificial Intelligence;Image
	Enhancement;Image Interpretation, Computer-Assisted;Imaging, Three-Dimensional;Information
	Storage and Retrieval;Pattern Recognition, Automated;Photography;Reproducibility
	of Results;Sensitivity and Specificity;}
}

@INPROCEEDINGS{se-local,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Local and Global Localization for Mobile Robots using Visual Landmarks},
  booktitle = {Proceedings of the IEEE/RSJ International Conference on Intelligent
	Robots and Systems },
  year = {2001},
  pages = {414--420},
  address = {Maui, Hawaii},
  month = {October},
  url = {citeseer.ist.psu.edu/se01local.html}
}

@ARTICLE{SLAM_Thrun_2004_5317,
  author = {<NAME>, <NAME>, <NAME>, <NAME>,
	<NAME>, <NAME>, <NAME>, <NAME>,
	<NAME>, <NAME>, <NAME>, and
	William } # Red # { <NAME>},
  journal = {Autonomous exploration and mapping of abandoned mines},
  year = {2004},
  volume = {11},
  pages = {79-91},
  number = {1},
  month = {December},
  booktitle = {IEEE Robotics and Automation Magazine},
  publisher = {IEEE}
}

@INPROCEEDINGS{REVIEW_GIST_SIA07,
  author = {<NAME>. and <NAME>.},
  title = {Biologically-inspired robotics vision monte-carlo localization in
	the outdoor environment},
  booktitle = {Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International
	Conference on},
  year = {2007},
  pages = {1723--1730},
  abstract = {We present a robot localization system using biologically-inspired
	vision. Our system models two extensively studied human visual capabilities:
	(1) extracting the "gist" of a scene to produce a coarse localization
	hypothesis, and (2) refining it by locating salient landmark regions
	in the scene. Gist is computed here as a holistic statistical signature
	of the image, yielding abstract scene classification and layout.
	Saliency is computed as a measure of interest at every image location,
	efficiently directing the time-consuming landmark identification
	process towards the most likely candidate locations in the image.
	The gist and salient landmark features are then further processed
	using a Monte-Carlo localization algorithm to allow the robot to
	generate its position. We test the system in three different outdoor
	environments - building complex (126times180 ft. area, 3794 testing
	images), vegetation-filled park (270times360 ft. area, 7196 testing
	images), and open-field park (450times585 ft. area, 8287 testing
	images) - each with its own challenges. The system is able to localize,
	on average, within 6.0, 10.73, and 32.24 ft., respectively, even
	with multiple kidnapped-robot instances.},
  citeulike-article-id = {3199748},
  citeulike-linkout-0 = {http://dx.doi.org/10.1109/IROS.2007.4399349},
  citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4399349},
  doi = {10.1109/IROS.2007.4399349},
  journal = {Intelligent Robots and Systems, 2007. IROS 2007. IEEE/RSJ International
	Conference on},
  keywords = {bioinspiration, image, landmarks, localization, robotics, saliency,
	vision},
  posted-at = {2008-09-06 14:21:05},
  priority = {0},
  url = {http://dx.doi.org/10.1109/IROS.2007.4399349}
}

@INPROCEEDINGS{WIFI,
  author = {<NAME> Sukhatme, <NAME>. and <NAME> },
  title = {Experiments in Monte-Carlo Localization using WiFi Signal Strength},
  booktitle = {Proceedings of the International Conference on Advanced Robotics},
  year = {2003},
  address = {Coimbra, Portugal},
  month = {Jul},
  citeulike-article-id = {1679026},
  keywords = {filter, probabilistic, wifi, wlan},
  posted-at = {2007-09-20 14:25:07},
  priority = {2},
  url = {http://cres.usc.edu/cgi-bin/print\_pub\_details.pl?pubid=341}
}

@ARTICLE{LOCAL_INVARIANT_FEATURES,
  author = {<NAME> and <NAME>},
  title = {Invariant feature histograms for texture classification},
  journal = {In Proceedings of the 1998 Joint Conference on Information Sciences
	(JCIS'98), Research Triangle Park, North Carolina, USA, October 1998.},
  year = {1998},
  url = {citeseer.ist.psu.edu/siggelkow98invariant.html}
}

@INPROCEEDINGS{EDGE_3,
  author = {<NAME> and <NAME>},
  title = {Learning and Evaluating Visual Features for Pose Estimation},
  booktitle = {{ICCV} (2)},
  year = {1999},
  pages = {1217-1222},
  url = {citeseer.ist.psu.edu/sim99learning.html}
}

@CONFERENCE{3D_MODELLING_soderman,
  author = {<NAME> and <NAME> and <NAME> and <NAME>},
  title = {Three-dimensional environment models from airborne laser radar data},
  year = {2004},
  editor = {<NAME>},
  volume = {5412},
  number = {1},
  pages = {333-344},
  publisher = {SPIE},
  doi = {10.1117/12.542487},
  journal = {Laser Radar Technology and Applications IX},
  location = {Orlando, FL, USA},
  url = {http://link.aip.org/link/?PSI/5412/333/1}
}

@INPROCEEDINGS{COLOR_INDEX_3,
  author = {<NAME> and <NAME> and <NAME> and <NAME>},
  title = {Content-based image retrieval using new color histogram},
  booktitle = {Intelligent Signal Processing and Communication Systems},
  year = {2004},
  pages = { 609-611},
  doi = {10.1109/ISPACS.2004.1439129},
  issn = { },
  journal = {Intelligent Signal Processing and Communication Systems, 2004. ISPACS
	2004. Proceedings of 2004 International Symposium on},
  keywords = { content-based retrieval, feature extraction, fuzzy set theory, image
	colour analysis, image representation, image retrieval RGB color
	components, color frequency value, color histogram intersection method,
	color quantization, color representation, content-based image retrieval,
	feature vectors, fuzzy theory, quantization error, similarity measurement}
}

@MISC{AIBO,
  author = {Sony},
  title = {{Sony AIBO Europe}},
  year = {1999},
  note = {http://support.sony-europe.com/aibo/},
  owner = {nghia},
  timestamp = {2008.12.17}
}

@INPROCEEDINGS{ADAPATIVE_PARTICLE_soto05adaptive,
  author = {<NAME>},
  title = {Self Adaptive Particle Filter},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year = {2005},
  citeulike-article-id = {1607045},
  keywords = {adaptive, filter, kld, particle, sampling},
  posted-at = {2007-08-30 11:38:40},
  priority = {0}
}

@INPROCEEDINGS{USER_LOCALIZATION_MAGNETIC_VISUAL,
  author = {<NAME> and <NAME> and <NAME> and <NAME>.
	Garrett and <NAME>},
  title = {Superior augmented reality registration by integrating landmark tracking
	and magnetic tracking},
  booktitle = {SIGGRAPH '96: Proceedings of the 23rd annual conference on Computer
	graphics and interactive techniques},
  year = {1996},
  pages = {429--438},
  address = {New York, NY, USA},
  publisher = {ACM},
  doi = {http://doi.acm.org/10.1145/237170.237282},
  isbn = {0-89791-746-4}
}

@ARTICLE{WAVELET_PRIMER,
  author = {<NAME>. and <NAME> <NAME>.},
  title = {Wavelets for computer graphics: a primer.1},
  journal = {Computer Graphics and Applications, IEEE},
  year = {May 1995},
  volume = {15},
  pages = {76-84},
  number = {3},
  doi = {10.1109/38.376616},
  issn = {0272-1716},
  keywords = {computer graphics, data compression, image coding, transforms, wavelet
	transformsHaar wavelets, coarse overall shape, computer graphics,
	hierarchical function decomposition, image compression, mathematical
	foundations}
}

@ARTICLE{REVIEW_VERTICAL_ALGORITHMS,
  author = {<NAME>},
  title = {Some location problems for robot navigation using a single camera},
  journal = {Comput. Vision Graph. Image Process.},
  year = {1988},
  volume = {42},
  pages = {112--129},
  number = {1},
  address = {San Diego, CA, USA},
  doi = {http://dx.doi.org/10.1016/0734-189X(88)90145-4},
  issn = {0734-189X},
  publisher = {Academic Press Professional, Inc.}
}

@INPROCEEDINGS{COLOR_INDEX_2,
  author = {<NAME>},
  title = {Interactive Indexing Into Image Databases},
  booktitle = {Storage and Retrieval for Image and Video Databases ({SPIE})},
  year = {1993},
  pages = {95-103},
  url = {citeseer.ist.psu.edu/swain93interactive.html}
}

@ARTICLE{COLOR_INDEXING_1,
  author = {<NAME> and <NAME>},
  title = {Color indexing},
  journal = {Int. J. Comput. Vision},
  year = {1991},
  volume = {7},
  pages = {11--32},
  number = {1},
  address = {Hingham, MA, USA},
  doi = {http://dx.doi.org/10.1007/BF00130487},
  issn = {0920-5691},
  publisher = {Kluwer Academic Publishers}
}

@INPROCEEDINGS{REVIEW_INVARIANT_Tamimi05-ams,
  author = {<NAME> and <NAME> and <NAME> and <NAME>},
  title = {Appearance-based Localization of Mobile Robots using Local Integral
	Invariants},
  booktitle = {In Proc. of the 9th International Conference on Intelligent Autonomous
	Systems (IAS-9)},
  year = {2006},
  pages = {181--188},
  address = {Tokyo, Japan}
}

@INPROCEEDINGS{REVIEW_INTEGRAL_2,
  author = {<NAME> and <NAME>},
  title = {Using Scale Space Image Histograms for Global Localization of Mobile
	Robots},
  booktitle = {In Proc. of the 36th International Symposium on Robotics (ISR)},
  year = {2005},
  address = {Tokyo, Japan}
}

@ARTICLE{SIFT_4,
  author = {<NAME> and <NAME>},
  title = {Global Robot Localization using Iterative Scale Invariant Feature
	Transform},
  journal = {36th International Symposium Robotics (ISR)},
  year = {2005}
}

@INPROCEEDINGS{REVIEW_KERNEL_PCA,
  author = {<NAME> and <NAME>},
  title = {Vision based Localization of Mobile Robots using Kernel approaches},
  booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
  year = {2004},
  owner = {nghia},
  timestamp = {2009.07.21}
}

@INPROCEEDINGS{taylor_2009_multiple,
  author = {<NAME> and <NAME>},
  title = {Multiple Target Localisation at over 100 FPS},
  booktitle = {British Machine Vision Conference},
  year = {2009},
  month = {September},
  notes = {Oral presentation},
  url = {http://mi.eng.cam.ac.uk/~sjt59/papers/taylor_2009_multiple.pdf}
}

@ARTICLE{USER_LOCALIZATION_2,
  author = {<NAME>. and <NAME>. and <NAME>.},
  title = {A wearable augmented reality system using positioning infrastructures
	and a pedometer},
  journal = {Wearable Computers, 2003. Proceedings. Seventh IEEE International
	Symposium on},
  year = {2003},
  pages = { 110-117},
  month = {Oct.},
  doi = {10.1109/ISWC.2003.1241400},
  issn = {1530-0811 }
}

@INPROCEEDINGS{3D_MODELLING_laser_comparison,
  author = {<NAME>., <NAME>.},
  title = {Performance Capabilities of Laser-Scanners - An Overview and Measurement
	Principle Analysis},
  booktitle = {INTERNATIONAL ARCHIVES OF PHOTOGRAMMETRY, REMOTE SENSING AND SPATIAL
	INFORMATION SCIENCES},
  year = {2004},
  owner = {nghia},
  timestamp = {2009.07.20}
}

@ARTICLE{USER_LOCALIZATION_ARQUAKE,
  author = {<NAME>. and <NAME>. and <NAME>. and <NAME>. and De
	Bondi, P. and <NAME>. and <NAME>.},
  title = {ARQuake: an outdoor/indoor augmented reality first person application},
  journal = {Wearable Computers, 2000. The Fourth International Symposium on},
  year = {2000},
  pages = {139-146},
  abstract = {This paper presents an outdoor/indoor augmented reality first person
	application ARQuake we have developed. ARQuake is an extension of
	the desktop game Quake, and as such we are investigating how to convert
	a desktop first person application into an outdoor/indoor mobile
	augmented reality application. We present an architecture for a low
	cost, moderately accurate six degrees of freedom tracking system
	based on GPS, digital compass, and fiducial vision-based tracking.
	Usability issues such as monster selection, colour, and input devices
	are discussed. A second application for AR architectural design visualisation
	is presented},
  doi = {10.1109/ISWC.2000.888480},
  keywords = {augmented reality, portable computersARQuake, architectural design
	visualisation, augmented reality, fiducial vision-based tracking,
	first person application, mobile augmented reality, outdoor/indoor,
	six degrees of freedom tracking system}
}

@INPROCEEDINGS{REVIEW_EDGE_Thompson93,
  author = {<NAME> and <NAME> and <NAME>
	and <NAME> and <NAME>},
  title = {Vision-Based Localization},
  booktitle = {In Proceedings of the 1993 Image Understanding Workshop},
  year = {1993},
  pages = {491--498},
  publisher = {ARPA, Morgan Kaufmann}
}

@ARTICLE{SLAM_survey,
  author = {<NAME>},
  title = {Robotic mapping: a survey},
  year = {2003},
  pages = {1--35},
  address = {San Francisco, CA, USA},
  book = {Exploring artificial intelligence in the new millennium},
  isbn = {1-55860-811-7},
  publisher = {Morgan Kaufmann Publishers Inc.}
}

@ARTICLE{MINERVA_1,
  author = {<NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME> and <NAME> and
	<NAME>},
  title = {MINERVA: a second-generation museum tour-guide robot},
  journal = {IEEE International Conference on Robotics and Automation},
  year = {1999},
  volume = {3},
  pages = {1999-2005 vol.3},
  doi = {10.1109/ROBOT.1999.770401},
  keywords = {humanities, interactive systems, mobile robots163 cm/s, MINERVA, Smithsonian
	museum, dynamic environments, interactive tour-guide robot, safe
	navigation, second-generation museum tour-guide robot, short-term
	human-robot interaction}
}

@ARTICLE{thrun01robust,
  author = {<NAME> and <NAME> and <NAME> and <NAME>},
  title = {Robust Monte Carlo localization for mobile robots},
  journal = {Artificial Intelligence},
  year = {2001},
  volume = {128},
  pages = {99-141},
  number = {1-2},
  url = {citeseer.ist.psu.edu/thrun01robust.html}
}

@ARTICLE{UAV_PANORAMIC_2,
  author = {<NAME> and <NAME> and <NAME> and Mandyam
	Srinivasan},
  title = {A Panoramic Stereo Imaging System for Aircraft Guidance},
  journal = {Australasian Conference on Robotics and Automation},
  year = {2007},
  owner = {nghia},
  timestamp = {2008.11.24}
}

@INPROCEEDINGS{3D_MODELLING_Tolt,
  author = {<NAME>. and <NAME>. and <NAME>.},
  title = {3D Urban Models from Laser Radar Data},
  booktitle = {Urban Remote Sensing Joint Event, 2007},
  year = {2007},
  pages = {1-5},
  month = {April},
  doi = {10.1109/URS.2007.371813},
  keywords = {database management systems, geographic information systems, information
	retrieval, optical radar, solid modelling, terrain mapping, virtual
	reality3D city models, 3D urban models, 3D virtual environment, airborne
	laser scanners, digital cameras, geographic feature information extraction,
	geographic information, geospecific terrain databases, laser radar
	data, remote sensing data}
}

@INPROCEEDINGS{REVIEW_TEXTURE,
  author = {<NAME>. and <NAME>. and <NAME>. and <NAME>.},
  title = {Context-based vision system for place and object recognition},
  booktitle = {Computer Vision, 2003. Proceedings. Ninth IEEE International Conference
	on},
  year = {2003},
  pages = {273-280 vol.1},
  month = {Oct.},
  abstract = {While navigating in an environment, a vision system has to be able
	to recognize where it is and what the main objects in the scene are.
	We present a context-based vision system for place and object recognition.
	The goal is to identify familiar locations (e.g., office 610, conference
	room 941, main street), to categorize new environments (office, corridor,
	street) and to use that information to provide contextual priors
	for object recognition (e.g., tables are more likely in an office
	than a street). We present a low-dimensional global image representation
	that provides relevant information for place recognition and categorization,
	and show how such contextual information introduces strong priors
	that simplify object recognition. We have trained the system to recognize
	over 60 locations (indoors and outdoors) and to suggest the presence
	and locations of more than 20 different object types. The algorithm
	has been integrated into a mobile system that provides realtime feedback
	to the user.},
  doi = {10.1109/ICCV.2003.1238354},
  keywords = {image representation, mobile robots, object detection, object recognition,
	robot visioncontext-based vision system, image categorization, low-dimensional
	global image representation, mobile system, object recognition, place
	recognition, realtime feedback}
}

@ARTICLE{Treuillet_bodymounted,
  author = {<NAME> and <NAME> and <NAME> and <NAME>
	and <NAME>},
  title = {Body mounted vision system for visually impaired outdoor and indoor
	wayfinding assistance},
  journal = {Conference \& Workshop on Assistive Technologies for People with
	Vision \& Hearing Impairments Assistive Technology for All Age},
  year = {2007}
}

@ARTICLE{CAMERA_CALIB,
  author = { <NAME>.},
  title = {A versatile camera calibration technique for high-accuracy 3D machine
	vision metrology using off-the-shelf TV cameras and lenses},
  journal = {Robotics and Automation, IEEE Journal of},
  year = {1987},
  volume = {3},
  pages = { 323-344},
  number = {4},
  month = {Aug},
  abstract = { A new technique for three-dimensional (3D) camera calibration for
	machine vision metrology using off-the-shelf TV cameras and lenses
	is described. The two-stage technique is aimed at efficient computation
	of camera external position and orientation relative to object reference
	coordinate system as well as the effective focal length, radial lens
	distortion, and image scanning parameters. The two-stage technique
	has advantage in terms of accuracy, speed, and versatility over existing
	state of the art. A critical review of the state of the art is given
	in the beginning. A theoretical framework is established, supported
	by comprehensive proof in five appendixes, and may pave the way for
	future research on 3D robotics vision. Test results using real data
	are described. Both accuracy and speed are reported. The experimental
	results are analyzed and compared with theoretical prediction. Recent
	effort indicates that with slight modification, the two-stage calibration
	can be done in real time.},
  issn = {0882-4967},
  keywords = {null Calibration, Machine vision, Measurement}
}

@ARTICLE{EIGENFACES,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Eigenfaces for recognition},
  journal = {J. Cognitive Neuroscience},
  year = {1991},
  volume = {3},
  pages = {71--86},
  number = {1},
  address = {Cambridge, MA, USA},
  issn = {0898-929X},
  publisher = {MIT Press}
}

@BOOK{Tuner1994,
  title = {In the eye’s mind : vision and the Helmholtz-Hering controversy},
  publisher = {Princeton University Press},
  year = {1994},
  author = {<NAME>},
  owner = {nghia},
  timestamp = {2009.07.21}
}

@INPROCEEDINGS{SIFT_8,
  author = {<NAME>. and <NAME>. and <NAME>. and <NAME>. and Jensfelt,
	R. and <NAME>.},
  title = {Towards robust place recognition for robot localization},
  booktitle = {Robotics and Automation, 2008. ICRA 2008. IEEE International Conference
	on},
  year = {2008},
  pages = {530-537},
  month = {May},
  abstract = {Localization and context interpretation are two key competences for
	mobile robot systems. Visual place recognition, as opposed to purely
	geometrical models, holds promise of higher flexibility and association
	of semantics to the model. Ideally, a place recognition algorithm
	should be robust to dynamic changes and it should perform consistently
	when recognizing a room (for instance a corridor) in different geographical
	locations. Also, it should be able to categorize places, a crucial
	capability for transfer of knowledge and continuous learning. In
	order to test the suitability of visual recognition algorithms for
	these tasks, this paper presents a new database, acquired in three
	different labs across Europe. It contains image sequences of several
	rooms under dynamic changes, acquired at the same time with a perspective
	and omnidirectional camera, mounted on a socket. We assess this new
	database with an appearance- based algorithm that combines local
	features with support vector machines through an ad-hoc kernel. Results
	show the effectiveness of the approach and the value of the database.},
  doi = {10.1109/ROBOT.2008.4543261},
  issn = {1050-4729},
  keywords = {image recognition, image sequences, learning (artificial intelligence),
	mobile robots, robot vision, support vector machinesad-hoc kernel,
	appearance-based algorithm, context interpretation, continuous learning,
	image sequences, mobile robot systems, robot localization, support
	vector machines, visual place recognition algorithm}
}

@INPROCEEDINGS{COLOUR_HISTOGRAM_1,
  author = {<NAME> and <NAME>},
  title = {Appearance-Based Place Recognition for Topological Localization},
  booktitle = {Proceedings of ICRA 2000},
  year = {2000},
  volume = {2},
  pages = {1023 - 1029},
  month = {April}
}

@INPROCEEDINGS{SURF_seasons,
  author = {<NAME> <NAME>.},
  title = {{SIFT}, {SURF} and Seasons: Long-term Outdoor Localization Using
	Local Features},
  booktitle = {Proceedings of the European Conference on Mobile Robots (ECMR)},
  year = {2007},
  pages = {253--258},
  month = {September 19--21},
  adress = {Freiburg, Germany}
}

@INPROCEEDINGS{INTEGRAL_IMAGES,
  author = {<NAME>. and <NAME>.},
  title = {Rapid object detection using a boosted cascade of simple features},
  year = {2001},
  volume = {1},
  pages = {I-511--I-518 vol.1},
  abstract = {This paper describes a machine learning approach for visual object
	detection which is capable of processing images extremely rapidly
	and achieving high detection rates. This work is distinguished by
	three key contributions. The first is the introduction of a new image
	representation called the "integral image" which allows the features
	used by our detector to be computed very quickly. The second is a
	learning algorithm, based on AdaBoost, which selects a small number
	of critical visual features from a larger set and yields extremely
	efficient classifiers. The third contribution is a method for combining
	increasingly more complex classifiers in a "cascade" which allows
	background regions of the image to be quickly discarded while spending
	more computation on promising object-like regions. The cascade can
	be viewed as an object specific focus-of-attention mechanism which
	unlike previous approaches provides statistical guarantees that discarded
	regions are unlikely to contain the object of interest. In the domain
	of face detection the system yields detection rates comparable to
	the best previous systems. Used in real-time applications, the detector
	runs at 15 frames per second without resorting to image differencing
	or skin color detection.},
  citeulike-article-id = {962012},
  citeulike-linkout-0 = {http://dx.doi.org/10.1109/CVPR.2001.990517},
  citeulike-linkout-1 = {http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=990517},
  doi = {10.1109/CVPR.2001.990517},
  journal = {Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings
	of the 2001 IEEE Computer Society Conference on},
  keywords = {adaboost, cascade, classifier, detection, feature, learning},
  posted-at = {2008-06-30 14:45:51},
  priority = {2},
  url = {http://dx.doi.org/10.1109/CVPR.2001.990517}
}

@INPROCEEDINGS{Vlassis96globalpath,
  author = {<NAME> and <NAME> and <NAME> and <NAME>
	and <NAME>},
  title = {Global path planning for autonomous qualitative navigation},
  booktitle = {Proc. 8th IEEE Int. Conf. on Tools with AI},
  year = {1996}
}

@ARTICLE{EDGE_1,
  author = {<NAME> and <NAME> and <NAME> and <NAME> andToshihiro
	Matsui},
  title = {Edge-based features from omnidirectional images for robot localization},
  journal = {IEEE InternationalConference on Robotics and Automation},
  year = {2001},
  volume = {2},
  pages = { 1579-1584 vol.2},
  doi = {10.1109/ROBOT.2001.932836},
  issn = {1050-4729 },
  keywords = { CCD image sensors, computerised navigation, edge detection, fast
	Fourier transforms, feature extraction, mobile robots, principal
	component analysis, robot vision FFT, Nomad XR4000 robot, PCA, Parzen
	density estimator, density estimate, edge detection, edge spatial
	density, edge-based features, fast Fourier transform, feature vector,
	low-dimensional feature extraction, omnidirectional images, principal
	component analysis, robot localization, robot navigation, sharp edge
	pixel location}
}

@INPROCEEDINGS{3D_MODELLING_vosselman,
  author = {<NAME>},
  title = {3D Building Model Reconstruction from Point Clouds and Ground Plans},
  booktitle = {INTERNATIONAL ARCHIVES OF PHOTOGRAMMETRY, REMOTE SENSING AND SPATIAL
	INFORMATION SCIENCES},
  year = {2001},
  owner = {nghia},
  timestamp = {2009.07.20}
}

@ARTICLE{AR_MOBILE,
  author = {<NAME>. and <NAME>. and <NAME>. and <NAME>. and
	<NAME>.},
  title = {Real-Time Detection and Tracking for Augmented Reality on Mobile
	Phones},
  journal = {Visualization and Computer Graphics, IEEE Transactions on},
  year = {2010},
  volume = {16},
  pages = {355 -368},
  number = {3},
  month = {may-june },
  doi = {10.1109/TVCG.2009.99},
  issn = {1077-2626},
  keywords = {feature descriptor;feature tracking;interactive frame rates;mobile
	phones;real-time detection;real-time tracking;state-of-the-art feature
	descriptors;template-matching-based tracker;augmented reality;mobile
	computing;mobile handsets;multimedia systems;real-time systems;vocabulary;Algorithms;Cellular
	Phone;Computer Graphics;Computer Systems;Image Interpretation, Computer-Assisted;Imaging,
	Three-Dimensional;Information Storage and Retrieval;Pattern Recognition,
	Automated;Software;User-Computer Interface;}
}

@INPROCEEDINGS{AR_PHONE_1,
  author = {<NAME>. and <NAME>. and <NAME>. and <NAME>. and
	<NAME>.},
  title = {Pose tracking from natural features on mobile phones},
  booktitle = {Mixed and Augmented Reality, 2008. ISMAR 2008. 7th IEEE/ACM International
	Symposium on},
  year = {2008},
  pages = {125 -134},
  month = {September},
  doi = {10.1109/ISMAR.2008.4637338},
  keywords = {Ferns feature descriptor;SIFT feature descriptor;augmented reality;current-generation
	phone;mobile phone;natural feature tracking;pose tracking;textured
	planar target;augmented reality;feature extraction;image texture;mobile
	computing;mobile handsets;pose estimation;target tracking;}
}

@INPROCEEDINGS{3D_wahl-2005-hybrid,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Identifying Planes in Point-Clouds for Efficient Hybrid Rendering},
  booktitle = {The 13th Pacific Conference on Computer Graphics and Applications},
  year = {2005},
  month = {October},
  conference = {The 13th Pacific Conference on Computer Graphics and Applications
	(Pacific Graphics 2005)},
  keywords = {hybrid rendering, point cloud, point rendering, plane detection, level-of-detail,
	out-of-core}
}

@ARTICLE{ICP_WALKER,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Estimating 3-D location parameters using dual number quaternions},
  journal = {CVGIP: Image Underst.},
  year = {1991},
  volume = {54},
  pages = {358--367},
  number = {3},
  address = {Orlando, FL, USA},
  doi = {http://dx.doi.org/10.1016/1049-9660(91)90036-O},
  issn = {1049-9660},
  publisher = {Academic Press, Inc.}
}

@MISC{KALMAN_2,
  author = {<NAME> and <NAME>},
  title = {An Introduction to the Kalman Filter},
  year = {1995},
  citeulike-article-id = {910851},
  keywords = {kalman, printed, tracking},
  posted-at = {2006-10-24 09:17:30},
  priority = {0},
  url = {http://www.cs.unc.edu/\~{}welch/kalman/kalmanIntro.html}
}

@ARTICLE{EDGE_2,
  author = {<NAME>. and <NAME>. and <NAME>. and <NAME>.},
  title = {Omni-directional vision for robot navigation},
  journal = {IEEE Workshop on Omnidirectional Vision},
  year = {2000},
  pages = {21-28}
}

@INPROCEEDINGS{JURGEN_WOLFRAM,
  author = {<NAME> and <NAME> and <NAME>},
  title = {Robust Vision-based Localization for Mobile Robots Using an Image
	Retrieval System Based on Invariant Features},
  booktitle = {IEEE International Conference on Robotics and Automation},
  year = {2002},
  url = {citeseer.ist.psu.edu/wolf02robust.html}
}

@INPROCEEDINGS{Xiong_Turkowski,
  author = {<NAME> and <NAME>},
  title = {Registration Calibration and Blending in Creating High Quality Panoramas},
  booktitle = {WACV '98: Proceedings of the 4th IEEE Workshop on Applications of
	Computer Vision (WACV'98)},
  year = {1998},
  pages = {69},
  address = {Washington, DC, USA},
  publisher = {IEEE Computer Society},
  isbn = {0-8186-8606-5}
}

@INPROCEEDINGS{GRID_BASED_PATH_FINDING,
  author = {<NAME>},
  title = {Grid-Based Path-Finding},
  booktitle = {AI '02: Proceedings of the 15th Conference of the Canadian Society
	for Computational Studies of Intelligence on Advances in Artificial
	Intelligence},
  year = {2002},
  pages = {44--55},
  address = {London, UK},
  publisher = {Springer-Verlag},
  isbn = {3-540-43724-X}
}

@INPROCEEDINGS{LINE_SEGMENT_MATCHING,
  author = {<NAME>. and <NAME>. and <NAME>.},
  title = {Line Segment Based Scan Matching for Concurrent Mapping and Localization
	of a Mobile Robot},
  booktitle = {Control, Automation, Robotics and Vision},
  year = {2006},
  pages = {1-6},
  month = {Dec},
  doi = {10.1109/ICARCV.2006.345101},
  journal = {Control, Automation, Robotics and Vision, 2006. ICARCV '06. 9th International
	Conference on},
  keywords = {conjugate gradient methods, control engineering computing, mobile
	robots, navigation, path planningC++ programming libraries, concurrent
	mapping, conjugate gradient method, laser scans, line segment based
	scan matching, mobile robot localization, multiple loop closing problem,
	navigational architecture, real-time robotic simulation environment}
}

@ARTICLE{SIFT_1,
  author = {<NAME> and <NAME> and <NAME> and <NAME>},
  title = {Mobile Robot Localization Using Fusion of Object Recognition and
	Range Information},
  journal = {Robotics and Automation, 2007 IEEE International Conference on},
  year = {10-14 April 2007},
  pages = {3533-3538},
  doi = {10.1109/ROBOT.2007.364019},
  issn = {1050-4729},
  keywords = {Monte Carlo methods, infrared detectors, mobile robots, object recognition,
	pose estimationIR scanner, Monte Carlo localization, SIFT based visual
	information, mobile robot localization, object recognition, range
	information, rough pose estimation, scale invariant feature transform}
}

@ARTICLE{3D_MODELLING_Zhao_CAD,
  author = {<NAME>. and <NAME>.},
  title = {Reconstructing a textured CAD model of an urban environment using
	Vehicle-borne Laser Range Scanners and Line Cameras},
  journal = MVA,
  year = {2003},
  volume = {14},
  pages = {35-41},
  number = {1},
  bibsource = {http://www.visionbib.com/bibliography/cartog941.html#TT90130}
}

@INPROCEEDINGS{zhuangnovel,
  author = {<NAME> and <NAME>},
  title = {Novel Multiresolution Metrics for Content-Based Image Retrieval},
  booktitle = {PG '97: Proceedings of the 5th Pacific Conference on Computer Graphics
	and Applications},
  year = {1997},
  pages = {105},
  address = {Washington, DC, USA},
  publisher = {IEEE Computer Society},
  isbn = {0-8186-8028-8}
}

@ARTICLE{SIFT_2,
  author = {<NAME> and <NAME>},
  title = {Vision and sonar sensor fusion for mobile robot localization in aliased
	environments},
  journal = {Mechatronic and Embedded Systems and Applications, Proceedings of
	the 2nd IEEE/ASME International Conference on},
  year = {Aug. 2006},
  pages = {1-6},
  doi = {10.1109/MESA.2006.296971},
  keywords = {SLAM (robots), image fusion, mobile robots, robot visionMonte Carlo
	localization, aliased environments, autonomous robots, mobile robotics,
	robot localization and navigation, sensor fusion, sensor modeling,
	sonar sensors, vision sensors}
}

@MISC{3D_MODELLING_tutorial,
  title = {OMC Technical Brief - Laser Time of Flight},
  note = {texttt{http://www.kuka.com/en/products/industrial_robots/}},
  owner = {nghia},
  timestamp = {2009.07.20}
}

@MISC{KUKA,
  title = {KUKA Industrial Robots},
  note = {texttt{http://www.kuka.com/en/products/industrial_robots/}},
  owner = {nghia},
  timestamp = {2008.12.17}
}

@ARTICLE{REVIEW_GIST_2,
  title = {Rapid Biologically-Inspired Scene Classification Using Features Shared
	with Visual Attention},
  journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
  year = {2007},
  volume = {29},
  pages = {300--312},
  number = {2},
  note = {Student Member-<NAME> and Member-Itti, Laurent},
  address = {Washington, DC, USA},
  doi = {http://dx.doi.org/10.1109/TPAMI.2007.40},
  issn = {0162-8828},
  publisher = {IEEE Computer Society}
}

