Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines

Abstract

We propose a fast and robust method to estimate the ground surface from LiDAR measurements on an automated vehicle. The ground surface is modeled as a uniform Bspline which is robust towards varying measurement densities and with a single parameter controlling the smoothness prior. We model the estimation process as a robust Least-Squares optimization problem which can be reformulated as a linear problem and thus solved efficiently. Using the SemanticKITTI data set, we conduct a quantitative evaluation by classifying the point-wise semantic annotations into ground and non-ground points. Finally, we validate the approach on our research vehicle in real-world scenarios.

A Rapid Segmentation Method of Highway Surface Point Cloud Data Based on a Supervoxel and Improved Region Growing Algorithm

“…They can effectively preserve the local features of the road boundaries. Therefore, B-spline curves were adopted for smoothing [44].…”