Moving Objects Tracking Based on Geometric Model-Free Approach With Particle Filter Using Automotive LiDAR
- Authors
- Lee, Hojoon; Lee, Hyunsung; Shin, Donghoon; Yi, Kyongsu
- Issue Date
- Oct-2022
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Autonomous vehicles; Autonomous vehicles; Laser radar; LiDAR; moving object state estimation; particle filter; Point cloud compression; Radar tracking; Shape; sparse point cloud.; Target tracking; Vehicle dynamics
- Citation
- IEEE Transactions on Intelligent Transportation Systems, v.23, no.10, pp 17863 - 17872
- Pages
- 10
- Journal Title
- IEEE Transactions on Intelligent Transportation Systems
- Volume
- 23
- Number
- 10
- Start Page
- 17863
- End Page
- 17872
- URI
- https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/146556
- DOI
- 10.1109/TITS.2022.3155828
- ISSN
- 1524-9050
1558-0016
- Abstract
- In this paper, we propose a Geometric Model-Free Approach with a Particle Filter (GMFA-PF) through the use of automotive LiDAR for real-time tracking of moving objects within an urban driving environment. GMFA-PF proved to be lightweight, capable of finishing the process within the sensing period of the LiDAR on a single CPU. The proposed GMFA-PF tracks and estimates moving objects without any assumptions on the geometry of the target. This approach enables efficient tracking of multiple object classes, with robustness to a sparse point cloud. Point cloud on moving objects is classified via the predicted Static Obstacle Map (STOM). A likelihood field is generated through the classified point cloud and is used in particle filtering to estimate the moving object's pose, shape, and speed. Quantitative and qualitative comparisons - with Geometric Model-Based Tracking (MBT), Deep Neural Network (DNN), and GMFA - are performed for GMFA-PF using urban driving and scenario driving data gathered on an autonomous vehicle fitted with close-to-market sensors. The proposed approach shows robust tracking and accurate estimation performance in both sparse and dense point clouds; GMFA-PF achieves improved tracking performance in dense traffic and reduces yaw estimation delay compared to the others. Autonomous vehicles with GMFA-PF demonstrated auto-nomous driving on urban roads. IEEE
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