Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional
  Auto-Encoder for Interest Point Detection and Feature Description   As an important technology in 3D mapping, autonomous driving, and robot
navigation, LiDAR odometry is still a challenging task. Appropriate data
structure and unsupervised deep learning are the keys to achieve an easy
adjusted LiDAR odometry solution with high performance. Utilizing compact 2D
structured spherical ring projection model and voxel model which preserves the
original shape of input data, we propose a fully unsupervised Convolutional
Auto-Encoder based LiDAR Odometry (CAE-LO) that detects interest points from
spherical ring data using 2D CAE and extracts features from multi-resolution
voxel model using 3D CAE. We make several key contributions: 1) experiments
based on KITTI dataset show that our interest points can capture more local
details to improve the matching success rate on unstructured scenarios and our
features outperform state-of-the-art by more than 50% in matching inlier ratio;
2) besides, we also propose a keyframe selection method based on matching pairs
transferring, an odometry refinement method for keyframes based on extended
interest points from spherical rings, and a backward pose update method. The
odometry refinement experiments verify the proposed ideas' feasibility and
effectiveness.

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The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph
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The categories of this paper are: funct-an
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The categories of this paper are: supr-con
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The categories of this paper are: physics
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The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org
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The categories of this paper are: plasm-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: quant-ph
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The categories of this paper are: econ
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The categories of this paper are: hep-th
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The categories of this paper are: chem-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: stat
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The categories of this paper are: math-ph