Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Optimization-Based Visual-Inertial SLAM Tightly Coupled with Raw GNSS
  Measurements   Unlike loose coupling approaches and the EKF-based approaches in the
literature, we propose an optimization-based visual-inertial SLAM tightly
coupled with raw Global Navigation Satellite System (GNSS) measurements, a
first attempt of this kind in the literature to our knowledge. More
specifically, reprojection error, IMU pre-integration error and raw GNSS
measurement error are jointly minimized within a sliding window, in which the
asynchronism between images and raw GNSS measurements is accounted for. In
addition, issues such as marginalization, noisy measurements removal, as well
as tackling vulnerable situations are also addressed. Experimental results on
public dataset in complex urban scenes show that our proposed approach
outperforms state-of-the-art visual-inertial SLAM, GNSS single point
positioning, as well as a loose coupling approach, including scenes mainly
containing low-rise buildings and those containing urban canyons.

14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat