Instance Segmentation of Sparse Point Clouds with Spatio-Temporal Coding for Autonomous Robot

Na Liu, Ye Yuan, Sai Zhang, Guodong Wu, Jie Leng and Lihong Wan ()
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Na Liu: Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China
Ye Yuan: Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China
Sai Zhang: Origin Dynamics Intelligent Robot Co., Ltd., Zhengzhou 450000, China
Guodong Wu: Origin Dynamics Intelligent Robot Co., Ltd., Zhengzhou 450000, China
Jie Leng: Institute of Machine Intelligence, University of Shanghai for Science and Technology, Shanghai 200093, China
Lihong Wan: Origin Dynamics Intelligent Robot Co., Ltd., Zhengzhou 450000, China

Mathematics, 2024, vol. 12, issue 8, 1-16

Abstract: In the study of Simultaneous Localization and Mapping (SLAM), the existence of dynamic obstacles will have a great impact on it, and when there are many dynamic obstacles, it will lead to great challenges in mapping. Therefore, segmenting dynamic objects in the environment is particularly important. The common data format in the field of autonomous robots is point clouds. How to use point clouds to segment dynamic objects is the focus of this study. The existing point clouds instance segmentation methods are mostly based on dense point clouds. In our application scenario, we use 16-line LiDAR (sparse point clouds) and propose a sparse point clouds instance segmentation method based on spatio-temporal encoding and decoding for autonomous robots in dynamic environments. Compared with other point clouds instance segmentation methods, the proposed algorithm has significantly improved average percision and average recall on instance segmentation of our point clouds dataset. In addition, the annotation of point clouds is time-consuming and laborious, and the existing dataset for point clouds instance segmentation is also very limited. Thus, we propose an autonomous point clouds annotation algorithm that integrates object tracking, segmentation, and point clouds to 2D mapping methods, the resulting data can then be used for training robust model.

Keywords: point clouds; dynamic targets; instance segmentation; spatio-temporal; autonomous robot (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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