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책임교수 정영식
논문명 Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud
논문종류 SCI
제1저자 Seoungjae Cho
교신저자 Sungdae Si
공동저자 Jonghyun Kim, Warda Ikram, Kyungeun Cho, Young-Sik Jeong, Kyhyun Um
Impact Factor 1.219
개제학술지명 Scientific World Journal
Keyword Ground Segmentation, Point Cloud, Light Detection and Ranging, Dynamic Flagmap, Voxels
게재일 2014 년 06 월
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.