Next Article in Journal
The Feature Extraction Based on Texture Image Information for Emotion Sensing in Speech
Next Article in Special Issue
High-Precision Image Aided Inertial Navigation with Known Features: Observability Analysis and Performance Evaluation
Previous Article in Journal
A Revised LRSPR Sensor with Sharp Reflection Spectrum
Previous Article in Special Issue
Sequential and Automatic Image-Sequence Registration of Road Areas Monitored from a Hovering Helicopter
Article Menu

Export Article

Open AccessArticle
Sensors 2014, 14(9), 16672-16691;

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

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, No.129 Luoyu Road, Wuhan 430079, China
Shenzhen Key Laboratory of Spatial-temporal Smart Sensing and Services, Shenzhen University, No.3688 Nanhai Road, Shenzhen 518060, China
School of Computer, Wuhan University, No.129 Luoyu Road, Wuhan 430072, China
School of Geodesy and Geomatics, Wuhan University, No.129 Luoyu Road, Wuhan 430079, China
Centre for Transport Studies (CTS), Imperial College London, Exhibition Road, London SW7 2AZ, UK
Authors to whom correspondence should be addressed.
Received: 16 July 2014 / Revised: 26 August 2014 / Accepted: 3 September 2014 / Published: 9 September 2014
(This article belongs to the Special Issue Positioning and Tracking Sensors and Technologies in Road Transport)
PDF [1110 KB, uploaded 9 September 2014]


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. View Full-Text
Keywords: 3D LiDAR; motion field estimation; motion sensing; spatial smoothing 3D LiDAR; motion field estimation; motion sensing; spatial smoothing
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Share & Cite This Article

MDPI and ACS Style

Li, Q.; Zhang, L.; Mao, Q.; Zou, Q.; Zhang, P.; Feng, S.; Ochieng, W. Motion Field Estimation for a Dynamic Scene Using a 3D LiDAR. Sensors 2014, 14, 16672-16691.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top