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Motion Field Estimation for a Dynamic Scene Using a 3D LiDAR

Authors :
Qingquan Li
Liang Zhang
Qingzhou Mao
Qin Zou
Pin Zhang
Shaojun Feng
Washington Ochieng
Source :
Sensors, Vol 14, Iss 9, Pp 16672-16691 (2014)
Publication Year :
2014
Publisher :
MDPI AG, 2014.

Abstract

This paper proposes a novel motion field estimation method based on a 3D light detection and ranging (LiDAR) sensor for motion sensing for intelligent driverless vehicles and active collision avoidance systems. Unlike multiple target tracking methods, which estimate the motion state of detected targets, such as cars and pedestrians, motion field estimation regards the whole scene as a motion field in which each little element has its own motion state. Compared to multiple target tracking, segmentation errors and data association errors have much less significance in motion field estimation, making it more accurate and robust. This paper presents an intact 3D LiDAR-based motion field estimation method, including pre-processing, a theoretical framework for the motion field estimation problem and practical solutions. The 3D LiDAR measurements are first projected to small-scale polar grids, and then, after data association and Kalman filtering, the motion state of every moving grid is estimated. To reduce computing time, a fast data association algorithm is proposed. Furthermore, considering the spatial correlation of motion among neighboring grids, a novel spatial-smoothing algorithm is also presented to optimize the motion field. The experimental results using several data sets captured in different cities indicate that the proposed motion field estimation is able to run in real-time and performs robustly and effectively.

Details

Language :
English
ISSN :
14248220
Volume :
14
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.56ec224d497347b481b49c197adadbb2
Document Type :
article
Full Text :
https://doi.org/10.3390/s140916672