Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud
Joint Authors
Cho, Seoungjae
Kim, Jonghyun
Ikram, Warda
Sim, Sungdae
Jeong, Young-Sik
Um, Kyhyun
Cho, Kyungeun
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-06-24
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
A ubiquitous environment for road travel that uses wireless networks requires the minimization of data exchange between vehicles.
An algorithm that can segment the ground in real time is necessary to obtain location data between vehicles simultaneously executing autonomous drive.
This paper proposes a framework for segmenting the ground in real time using a sparse three-dimensional (3D) point cloud acquired from undulating terrain.
A sparse 3D point cloud can be acquired by scanning the geography using light detection and ranging (LiDAR) sensors.
For efficient ground segmentation, 3D point clouds are quantized in units of volume pixels (voxels) and overlapping data is eliminated.
We reduce nonoverlapping voxels to two dimensions by implementing a lowermost heightmap.
The ground area is determined on the basis of the number of voxels in each voxel group.
We execute ground segmentation in real time by proposing an approach to minimize the comparison between neighboring voxels.
Furthermore, we experimentally verify that ground segmentation can be executed at about 19.31 ms per frame.
American Psychological Association (APA)
Cho, Seoungjae& Kim, Jonghyun& Ikram, Warda& Cho, Kyungeun& Jeong, Young-Sik& Um, Kyhyun…[et al.]. 2014. Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1050195
Modern Language Association (MLA)
Cho, Seoungjae…[et al.]. Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud. The Scientific World Journal No. 2014 (2014), pp.1-9.
https://search.emarefa.net/detail/BIM-1050195
American Medical Association (AMA)
Cho, Seoungjae& Kim, Jonghyun& Ikram, Warda& Cho, Kyungeun& Jeong, Young-Sik& Um, Kyhyun…[et al.]. Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-9.
https://search.emarefa.net/detail/BIM-1050195
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1050195