Sloped terrain segmentation for autonomous drive using sparse 3D point cloud

Seoungjae Cho, Jonghyun Kim, Warda Ikram, Kyungeun Cho, Young Sik Jeong, Kyhyun Um, Sungdae Sim

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

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.

Original languageEnglish
Article number582753
JournalScientific World Journal
Volume2014
DOIs
StatePublished - 2014

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