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

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 targ...

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Detalles Bibliográficos
Autores principales: Li, Qingquan, Zhang, Liang, Mao, Qingzhou, Zou, Qin, Zhang, Pin, Feng, Shaojun, Ochieng, Washington
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208193/
https://www.ncbi.nlm.nih.gov/pubmed/25207868
http://dx.doi.org/10.3390/s140916672
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author Li, Qingquan
Zhang, Liang
Mao, Qingzhou
Zou, Qin
Zhang, Pin
Feng, Shaojun
Ochieng, Washington
author_facet Li, Qingquan
Zhang, Liang
Mao, Qingzhou
Zou, Qin
Zhang, Pin
Feng, Shaojun
Ochieng, Washington
author_sort Li, Qingquan
collection PubMed
description 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.
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spelling pubmed-42081932014-10-24 Motion Field Estimation for a Dynamic Scene Using a 3D LiDAR Li, Qingquan Zhang, Liang Mao, Qingzhou Zou, Qin Zhang, Pin Feng, Shaojun Ochieng, Washington Sensors (Basel) Article 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. MDPI 2014-09-09 /pmc/articles/PMC4208193/ /pubmed/25207868 http://dx.doi.org/10.3390/s140916672 Text en © 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/).
spellingShingle Article
Li, Qingquan
Zhang, Liang
Mao, Qingzhou
Zou, Qin
Zhang, Pin
Feng, Shaojun
Ochieng, Washington
Motion Field Estimation for a Dynamic Scene Using a 3D LiDAR
title Motion Field Estimation for a Dynamic Scene Using a 3D LiDAR
title_full Motion Field Estimation for a Dynamic Scene Using a 3D LiDAR
title_fullStr Motion Field Estimation for a Dynamic Scene Using a 3D LiDAR
title_full_unstemmed Motion Field Estimation for a Dynamic Scene Using a 3D LiDAR
title_short Motion Field Estimation for a Dynamic Scene Using a 3D LiDAR
title_sort motion field estimation for a dynamic scene using a 3d lidar
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4208193/
https://www.ncbi.nlm.nih.gov/pubmed/25207868
http://dx.doi.org/10.3390/s140916672
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