Imperial College London
Alternate

DrShaojunFeng

Faculty of Engineering, Department of Civil and Environmental Engineering

Honorary Principal Research Fellow
 
 
 
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Contact

 

s.feng

 
 
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Location

 

618Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Li:2014:10.3390/s140916672,
author = {Li, Q and Zhang, L and Mao, Q and Zou, Q and Zhang, P and Feng, S and Ochieng, W},
doi = {10.3390/s140916672},
journal = {Sensors},
pages = {16672--16691},
title = {Motion field estimation for a dynamic scene using a 3D LiDAR},
url = {http://dx.doi.org/10.3390/s140916672},
volume = {14},
year = {2014}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Li,Q
AU  - Zhang,L
AU  - Mao,Q
AU  - Zou,Q
AU  - Zhang,P
AU  - Feng,S
AU  - Ochieng,W
DO  - 10.3390/s140916672
EP  - 16691
PY  - 2014///
SN  - 1424-2818
SP  - 16672
TI  - Motion field estimation for a dynamic scene using a 3D LiDAR
T2  - Sensors
UR  - http://dx.doi.org/10.3390/s140916672
UR  - https://www.ncbi.nlm.nih.gov/pubmed/25207868
UR  - http://hdl.handle.net/10044/1/50970
VL  - 14
ER  -
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