Abstract
Estimating the traversability of terrain in an unstructured outdoor environment is one of the challenging issues in autonomous vehicles. When dealing with a large 3D point cloud, the computational cost of processing all of the individual points is very high. Thus voxelization methods are used extensively. In this paper, we propose a more fine-grained voxelization algorithm in the context of unstructured terrain classification. While the current shape of a voxel is a fixed-length cubic, we construct a flexible shape voxel which has spatial and geometrical properties. Furthermore, we propose a new shape histogram feature that represents the statistical characteristics of 3D points. The proposed method was tested using data obtained from unstructured outdoor environments for performance evaluation.
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© 2015 Springer International Publishing Switzerland
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Song, S., Jo, S. (2015). Traversability Classification Using Super-voxel Method in Unstructured Terrain. In: Kim, JH., Yang, W., Jo, J., Sincak, P., Myung, H. (eds) Robot Intelligence Technology and Applications 3. Advances in Intelligent Systems and Computing, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-319-16841-8_53
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DOI: https://doi.org/10.1007/978-3-319-16841-8_53
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16840-1
Online ISBN: 978-3-319-16841-8
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