JRM Vol.35 No.2 pp. 424-434
doi: 10.20965/jrm.2023.p0424


Optimal Clustering of Point Cloud by 2D-LiDAR Using Kalman Filter

Shuncong Shen*1, Mai Saito*1, Yuka Uzawa*2, and Toshio Ito*3,*4

*1Department of Machinery and Control Systems, Graduate School of Systems Engineering and Science, Shibaura Institute of Technology
307 Fukasaku, Minuma-ku, Saitama City, Saitama 337-8570, Japan

*2College of Systems Engineering and Science, Shibaura Institute of Technology
307 Fukasaku, Minuma-ku, Saitama City, Saitama 337-8570, Japan

*3Department of Machinery and Control Systems, Shibaura Institute of Technology
307 Fukasaku, Minuma-ku, Saitama City, Saitama 337-8570, Japan

*4Hyper Digital Twins Co., Ltd.
2-1-17 Nihonbashi, Chuo-ku, Tokyo 103-0027, Japan

October 20, 2022
January 25, 2023
April 20, 2023
2D-LiDAR, autonomous mobility, Kalman filter, clustering, navigation

Light detection and ranging (LiDAR) has been the primary sensor for autonomous mobility and navigation system owing to its stability. Although multiple-channel LiDAR (3D-LiDAR) can obtain dense point clouds that provide optimal performance for several tasks, the application scope is limited by its high-cost. When employing single channel LiDAR (2D-LiDAR) as a low-cost alternative, the quantity and quality of the point cloud cause conventional methods to perform poorly in clustering and tracking tasks. In particular, when handling multiple pedestrian scenarios, the point cloud is not distinguished and clustering is unable to succeed. Hence, we propose an optimized clustering method combined with a Kalman filter (KF) for simultaneous clustering and tracking applicable to 2D-LiDAR.

Results with our Kalman filter-based method vs. w/o

Results with our Kalman filter-based method vs. w/o

Cite this article as:
S. Shen, M. Saito, Y. Uzawa, and T. Ito, “Optimal Clustering of Point Cloud by 2D-LiDAR Using Kalman Filter,” J. Robot. Mechatron., Vol.35 No.2, pp. 424-434, 2023.
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Last updated on Sep. 21, 2023