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The Scientific World Journal
Volume 2014, Article ID 582753, 9 pages
http://dx.doi.org/10.1155/2014/582753
Research Article

Sloped Terrain Segmentation for Autonomous Drive Using Sparse 3D Point Cloud

1Department of Multimedia Engineering, Dongguk University-Seoul, Seoul 100-715, Republic of Korea
2Agency for Defense Development, Daejeon 305-152, Republic of Korea

Received 5 April 2014; Accepted 5 June 2014; Published 24 June 2014

Academic Editor: Jong-Hyuk Park

Copyright © 2014 Seoungjae Cho et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.