Research Article
Improving Accuracy of Mobile Robot Localization by Tightly Fusing LiDAR and DR data
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@INPROCEEDINGS{10.1007/978-3-030-51103-6_10, author={Yuan Xu and Yuriy S. Shmaliy and Tao Shen and Shuhui Bi and Hang Guo}, title={Improving Accuracy of Mobile Robot Localization by Tightly Fusing LiDAR and DR data}, proceedings={Multimedia Technology and Enhanced Learning. Second EAI International Conference, ICMTEL 2020, Leicester, UK, April 10-11, 2020, Proceedings, Part II}, proceedings_a={ICMTEL PART 2}, year={2020}, month={7}, keywords={Light detection and ranging (LiDAR) Dead Reckoning (DR) Tightly integration Uncertain sampling period}, doi={10.1007/978-3-030-51103-6_10} }- Yuan Xu
Yuriy S. Shmaliy
Tao Shen
Shuhui Bi
Hang Guo
Year: 2020
Improving Accuracy of Mobile Robot Localization by Tightly Fusing LiDAR and DR data
ICMTEL PART 2
Springer
DOI: 10.1007/978-3-030-51103-6_10
Abstract
In this paper, a tightly-coupled light detection and ranging (LiDAR)/dead reckoning (DR) navigation system with uncertain sampling time is designed for mobile robot localization. The Kalman filter (KF) is used as the main data fusion filter, where the state vector is composed of the position error, velocity error, yaw, and sampling time. The observation is provided of the difference between the LiDAR-derived and DR-derived distances measured from the corner feature points (CFPs) to the mobile robot. A real test experiment has been conducted to verify a good performance of the proposed method and show that it allows for a higher accuracy compared to the traditional LiDAR/DR integration.
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