High-Frequency Ground Segmentation for Autonomous Mobile Robots: A RANSAC-Based Approach
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Abstract
In this work, a RANSAC-based algorithm was developed for ground segmentation on point clouds obtained from 3D LIDAR sensors. The algorithm employs both distance and normal angle criteria to construct a robust ground plane model, even in the presence of noise and outliers. In the initial stage, a height filter is applied to analyze only the points associated with the ground. Subsequently, the RANSAC method identifies the plane model with the highest number of inliers, dividing the point cloud into two groups: ground and obstacles.
The proposed method demonstrated real-time performance with a 20 Hz LIDAR sensor, delivering higher speed and accuracy compared to alternative approaches. This study provides an effective and reliable solution for ground segmentation in autonomous systems.
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References
Keqi Zhang, Bryan C. Bourgeois, and David W. Collins, "Progressive Morphological Filter (PMF) Algorithm for Terrain Extraction from Airborne LIDAR Data," Journal of Photogrammetric Engineering & Remote Sensing, vol. 69, no. 4, pp. 399-406, 2003. DOI: https://doi.org/10.1109/TGRS.2003.810682
Mohd Isa, N. A., Mohamad, N., Yusoff, N. M., and Zabidi, H., "Application of RANSAC Algorithm in Plane Segmentation for Autonomous Driving," IOP Conference Series: Materials Science and Engineering, vol. 530, 2019.