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Detail
Journal Article
Toward Accurate Road Detection in Challenging Environments Using 3D Point Clouds
- Authors
- Jaemin Byun, Beom-Su Seo, Jihong Lee
- Issue Date
- 2015-06
- Citation
- ETRI Journal, v.37, no.3, pp.606-616
- ISSN
- 1225-6463
- Publisher
- 한국전자통신연구원 (ETRI)
- Language
- English
- Type
- Journal Article
- Abstract
- In this paper, we propose a novel method for road recognition using 3D point clouds based on a Markov random field (MRF) framework in unstructured and complex road environments. The proposed method is focused on finding a solution for an analysis of traversable regions in challenging environments without considering an assumption that has been applied in many past studies; that is, that the surface of a road is ideally flat. The main contributions of this research are as follows: (a) guidelines for the best selection of the gradient value, the average height, the normal vectors, and the intensity value and (b) how to mathematically transform a road recognition problem into a classification problem that is based on MRF modeling in spatial and visual contexts. In our experiments, we used numerous scans acquired by an HDL-64E sensor mounted on an experimental vehicle. The results show that the proposed method is more robust and reliable than a conventional approach based on a quantity evaluation with ground truth data for a variety of challenging environments.
- KSP Keywords
- 3D point cloud, Classification problems, Complex road, Ground truth data, Markov Random Field, Recognition problem, Road recognition, experimental vehicle, gradient value, normal vector, novel method