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

Sensors

Qingquan Li²  Liang Zhang²  Qingzhou Mao²  Qin Zou¹  Pin Zhang⁴  Shaojun Feng³ 

[1] School of Computer, Wuhan University, No.129 Luoyu Road, Wuhan 430072, China;State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, No.129 Luoyu Road, Wuhan 430079, China; E-Mail:;Centre for Transport Studies (CTS), Imperial College London, Exhibition Road, London SW7 2AZ, UK; E-Mails:;School of Geodesy and Geomatics, Wuhan University, No.129 Luoyu Road, Wuhan 430079, China; E-Mail:

关键词: 3D LiDAR;    motion field estimation;    motion sensing;    spatial smoothing;   

DOI  :  10.3390/s140916672

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.