Imperial College London
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DrSenWang

Faculty of Engineering, Department of Electrical and Electronic Engineering

Senior Lecturer
 
 
 
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Contact

 

sen.wang

 
 
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Location

 

Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Li:2023:10.1177/01423312221105165,
author = {Li, C and Yan, F and Wang, S and Zhuang, Y},
doi = {10.1177/01423312221105165},
journal = {Transactions of the Institute of Measurement and Control},
pages = {274--286},
title = {A 3D LiDAR odometry for UGVs using coarse-to-fine deep scene flow estimation},
url = {http://dx.doi.org/10.1177/01423312221105165},
volume = {45},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Light detection and ranging (LiDAR) odometry plays a crucial role in autonomous mobile robots and unmanned ground vehicles (UGVs). This paper presents a deep learning–based odometry system using two successive three-dimensional (3D) point clouds to estimate their scene flow and then predict their relative pose. The network consumes continuous 3D point clouds directly and outputs their scene flow and uncertain mask in a coarse-to-fine fashion. A pose estimation layer without trainable parameters is designed to compute the pose with the scene flow. We also introduce a scan-to-map optimization algorithm to enhance the robustness and accuracy of the system. Our experiments on the KITTI odometry data set and our campus data set demonstrate the effectiveness of the proposed deep learning–based point cloud odometry.
AU  - Li,C
AU  - Yan,F
AU  - Wang,S
AU  - Zhuang,Y
DO  - 10.1177/01423312221105165
EP  - 286
PY  - 2023///
SN  - 0142-3312
SP  - 274
TI  - A 3D LiDAR odometry for UGVs using coarse-to-fine deep scene flow estimation
T2  - Transactions of the Institute of Measurement and Control
UR  - http://dx.doi.org/10.1177/01423312221105165
VL  - 45
ER  -
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