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학술지 Toward Accurate Road Detection in Challenging Environments Using 3D Point Clouds
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변재민, 서범수, 이지홍
ETRI Journal, v.37 no.3, pp.606-616
한국전자통신연구원 (ETRI)
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 제안 키워드
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