A Slope-robust Cascaded Ground Segmentation in 3D Point Cloud for Autonomous Vehicles
By Patiphon Narksri, Eijiro Takeuchi, Yoshiki Ninomiya, Luis Yoichi Morales Saiki, Naoki Akai, Nobuo Kawaguchi. 2018
88 citations recorded by the source corpus.
In this paper, a slope-robust cascaded ground segmentation in 3D point cloud for autonomous vehicles is presented. In many challenging terrains encountered by autonomous vehicles where the ground does not have a simple planar shape such as sloped roads, many existing ground segmentation algorithms fail. The proposed algorithm aims to correctly segment ground points in scans where these challenging terrains are present. The proposed method consists of two main steps. First, filtering the majority of non-ground points using the geometry of the sensor and the distance between consecutive rings in the scan. In the second step, multi-region RANSAC plane fitting is used to separate remaining non-ground points from ground points in the scan. The 3D data was taken and partially labeled for quantitative evaluation. The experimental results were outstanding as the proposed algorithm could segment the ground correctly in various challenging terrains. The proposed algorithm could correctly segment ground points in the scan even in sloped terrains and achieved higher accuracy than other algorithms used in the evaluation.
Subfields: Aerospace Engineering, Automotive Engineering, Environmental Engineering
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Citations in the corpus
- A Survey of Autonomous Driving: Common Practices and Emerging Technologies (2019, methodology): “as ground filtering [160] and map-based filtering [161].”