Moving Objects Tracking Based on Geometric Model-Free Approach With Particle Filter Using Automotive LiDAR

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...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2022-10, Vol.23 (10), p.17863-17872
Main Authors: Lee, Hojoon, Lee, Hyunsung, Shin, Donghoon, Yi, Kyongsu
Format: Article
Language:English
Subjects:
Artificial neural networks
Automobiles
Autonomous vehicles
Driving
Laser radar
LiDAR
Moving object recognition
moving object state estimation
particle filter
Point cloud compression
Radar tracking
Shape
sparse point cloud
Target tracking
Tracking
Vehicle dynamics
Yaw
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Summary: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.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2022.3155828