Fusion of Velodyne and camera data for scene parsing

The fusion of information gathered from multiple sources is essential to build a comprehensive situation picture for autonomous ground vehicles. In this paper, an approach which performs scene classification and data fusion for 3D-LIDAR scanner (Velodyne HDL-64E) and video camera is described. A geo...

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Main Authors: Zhao, Gangqiang, Xiao, Xuhong, Yuan, Junsong
Other Authors: School of Electrical and Electronic Engineering
Format: Conference or Workshop Item
Language:English
Published: 2013
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Online Access:https://hdl.handle.net/10356/100802
http://hdl.handle.net/10220/17984
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6289941
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1008022019-12-06T20:28:34Z Fusion of Velodyne and camera data for scene parsing Zhao, Gangqiang Xiao, Xuhong Yuan, Junsong School of Electrical and Electronic Engineering International Conference on Information Fusion (15th : 2012 : Singapore) DRNTU::Engineering::Electrical and electronic engineering The fusion of information gathered from multiple sources is essential to build a comprehensive situation picture for autonomous ground vehicles. In this paper, an approach which performs scene classification and data fusion for 3D-LIDAR scanner (Velodyne HDL-64E) and video camera is described. A geometry segmentation algorithm is proposed for detection of obstacles and ground area from data collected by the Velodyne. In the meantime, the corresponding image collected by video camera is classified patch by patch into more detailed categories. The final situation picture is obtained by fusing the classification results of Velodyne data and that of images using the fuzzy logic inference framework. The proposed approach was evaluated with datasets collected by our autonomous ground vehicle testbed in the rural area. The fused results are more reliable and more completable than those provided by individual sensors. Accepted version 2013-12-02T08:25:35Z 2019-12-06T20:28:34Z 2013-12-02T08:25:35Z 2019-12-06T20:28:34Z 2012 2012 Conference Paper Zhao, G., Xiao, X., & Yuan, J. (2012). Fusion of Velodyne and Camera Data for Scene Parsing. 15th International Conference on Information Fusion (FUSION), pp.1172-1179. https://hdl.handle.net/10356/100802 http://hdl.handle.net/10220/17984 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6289941 en © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6289941]. 8 p. This work was supported in part by the DSO-NTU project M4060969.040, as well as Nanyang Assistant Professorship to Dr. Junsong Yuan. application/pdf
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic DRNTU::Engineering::Electrical and electronic engineering
spellingShingle DRNTU::Engineering::Electrical and electronic engineering
Zhao, Gangqiang
Xiao, Xuhong
Yuan, Junsong
Fusion of Velodyne and camera data for scene parsing
description The fusion of information gathered from multiple sources is essential to build a comprehensive situation picture for autonomous ground vehicles. In this paper, an approach which performs scene classification and data fusion for 3D-LIDAR scanner (Velodyne HDL-64E) and video camera is described. A geometry segmentation algorithm is proposed for detection of obstacles and ground area from data collected by the Velodyne. In the meantime, the corresponding image collected by video camera is classified patch by patch into more detailed categories. The final situation picture is obtained by fusing the classification results of Velodyne data and that of images using the fuzzy logic inference framework. The proposed approach was evaluated with datasets collected by our autonomous ground vehicle testbed in the rural area. The fused results are more reliable and more completable than those provided by individual sensors.
author2 School of Electrical and Electronic Engineering
author_facet School of Electrical and Electronic Engineering
Zhao, Gangqiang
Xiao, Xuhong
Yuan, Junsong
format Conference or Workshop Item
author Zhao, Gangqiang
Xiao, Xuhong
Yuan, Junsong
author_sort Zhao, Gangqiang
title Fusion of Velodyne and camera data for scene parsing
title_short Fusion of Velodyne and camera data for scene parsing
title_full Fusion of Velodyne and camera data for scene parsing
title_fullStr Fusion of Velodyne and camera data for scene parsing
title_full_unstemmed Fusion of Velodyne and camera data for scene parsing
title_sort fusion of velodyne and camera data for scene parsing
publishDate 2013
url https://hdl.handle.net/10356/100802
http://hdl.handle.net/10220/17984
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6289941
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