Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the
  Normal Distribution Transform Representation   3D point cloud-based place recognition is highly demanded by autonomous
driving in GPS-challenged environments and serves as an essential component
(i.e. loop-closure detection) in lidar-based SLAM systems. This paper proposes
a novel approach, named NDT-Transformer, for realtime and large-scale place
recognition using 3D point clouds. Specifically, a 3D Normal Distribution
Transform (NDT) representation is employed to condense the raw, dense 3D point
cloud as probabilistic distributions (NDT cells) to provide the geometrical
shape description. Then a novel NDT-Transformer network learns a global
descriptor from a set of 3D NDT cell representations. Benefiting from the NDT
representation and NDT-Transformer network, the learned global descriptors are
enriched with both geometrical and contextual information. Finally, descriptor
retrieval is achieved using a query-database for place recognition. Compared to
the state-of-the-art methods, the proposed approach achieves an improvement of
7.52% on average top 1 recall and 2.73% on average top 1% recall on the Oxford
Robotcar benchmark.

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The categories of this paper are: gr-qc
11
The categories of this paper are: hep-lat
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The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
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The categories of this paper are: adap-org
9
The categories of this paper are: nlin
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
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The categories of this paper are: hep-th
37
The categories of this paper are: supr-con
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph
29
The categories of this paper are: funct-an
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The categories of this paper are: stat
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The categories of this paper are: cond-mat