Motion field estimation for a dynamic scene using a 3D LiDAR
File(s)
Author(s)
Type
Journal Article
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.
Date Issued
2014-09-09
Date Acceptance
2014-09-03
Citation
Sensors, 2014, 14 (9), pp.16672-16691
ISSN
1424-2818
Publisher
MDPI AG
Start Page
16672
End Page
16691
Journal / Book Title
Sensors
Volume
14
Issue
9
Copyright Statement
© 2014 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article
distributed under the terms and conditions of the Creative Commons Attribution license
(http://creativecommons.org/licenses/by/3.0/).
distributed under the terms and conditions of the Creative Commons Attribution license
(http://creativecommons.org/licenses/by/3.0/).
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/25207868
PII: s140916672
Subjects
0301 Analytical Chemistry
0906 Electrical And Electronic Engineering
Analytical Chemistry
Publication Status
Published
Coverage Spatial
Switzerland