LiDAR-based simultaneous multi-object tracking and static mapping in nearshore scenario. (15th March 2023)

• Record Type: Journal Article
• Authors: Yao, Zhiting; Chen, Xiyuan; Xu, Ninghui; Gao, Ning; Ge, Mingming
• Is Part Of: Ocean engineering. Volume 272(2023)
• Publication Date: 2023-03-15
• Subjects: Maritime autonomous surface ship -- 3D LiDAR -- Multi-object tracking -- Environment perception; Ocean engineering -- Periodicals; Ocean engineering; Periodicals; 620.4162
• DOI: 10.1016/j.oceaneng.2023.113939
• Languages: English
• ISSNs: 0029-8018

Abstract

Abstract: Reliable object tracking is a key ingredient for maritime autonomous surface ship (MASS), which can perceive surrounding objects and predict their motion state. Existing tracking algorithms mainly rely on visual sensor, and their application background is predominantly concentrated in the field of unmanned vehicles. In this paper, a scheme of directly processing LiDAR point cloud is proposed to achieve accurate and stable multi-object tracking in nearshore scenario. Firstly, based on the point cloud clustering results, an object position and volume estimation method is investigated. Then, the geometric distribution and 3D shape descriptors are conceived to construct the time-varying affinity matrix. The data association results are forwarded into a Kalman Filter based tracker life management mechanism. Finally, the remaining scattered points and unqualified trackers are composed into a point set, which is then modeled by convex polygons. The real-world experiment results confirm the effectiveness of the proposed maritime perception framework in tracking objects and mapping the ambience in a bustling lake. Compared with annotated ground-truth, the average multi-object tracking precision and accuracy is 0.69 m and 85%, respectively. And the average computational frequency achieves 29Hz. The proposed scheme successfully applies LiDAR to the field of sea surface object tracking, which provides a practical and meaningful reference for the application of LiDAR in MASS. Highlights: A water surface perception framework is proposed and thoroughly verified in real-world experiments. Geometric distribution and shape feature descriptors are served as a bridge for matching detected objects across frames. A parallel scan-by-scan object detection and tracking mode is designed to satisfy real-time environmental surveillance.