Fast and Robust Ground Surface Estimation from LIDAR Measurements using Uniform B-Splines
This addresses ground surface estimation for automated vehicles, but it appears incremental as it builds on existing methods with specific improvements.
The paper tackles the problem of estimating ground surfaces from LIDAR data for automated vehicles by proposing a fast and robust method using uniform B-splines, achieving efficient performance validated on the SemanticKITTI dataset and real-world scenarios.
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 UBS which is robust towards varying measurement densities and with a single parameter controlling the smoothness prior. We model the estimation process as a robust LS 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.