A road segmentation method based on reorganized LiDAR points and line scanning

Zhihui Wang; Hao Gu; Ning Yin

doi:10.1117/12.2601067

30 June 2021 A road segmentation method based on reorganized LiDAR points and line scanning

Zhihui Wang, Hao Gu, Ning Yin

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 118780S (2021) https://doi.org/10.1117/12.2601067
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

Road segmentation is an important part of autonomous driving vehicles. Reliable road segmentation results are a prerequisite for autonomous driving tasks, e.g., path planning. In this paper, we propose a road segmentation method based on 3D point cloud organization and line scanning of LiDAR data. Our model assumes that road areas are always flatter than non-road areas. First, we propose an adjacent-line-difference (ALD) feature to define the flatness of the point cloud. Using this feature, the approximate road area can be estimated. Then, we proposed a horizontal and vertical scanning strategy to obtain a more accurate road area. In order to prove the effectiveness of our method, experiments are conducted on the public KITTI-Road benchmark. The experimental results prove that the proposed approach can achieve one of the best performances among all LiDAR-based methods.

Citation Download Citation

Zhihui Wang, Hao Gu, and Ning Yin "A road segmentation method based on reorganized LiDAR points and line scanning", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 118780S (30 June 2021); https://doi.org/10.1117/12.2601067

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