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

arXiv: https://arxiv.org/abs/2203.01180 · HTML: https://mlsanity.com/paper/2203.01180/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 11 references that are in the map.
- First posted on arXiv 2 Mar 2022.

## Builds on (most cited first, 1 of 1)

- [Graduated Non-Convexity for Robust Spatial Perception: From Non-Minimal Solvers to Global Outlier Rejection](https://mlsanity.com/paper/1909.08605/) (2019)

## Built on by (most cited first, 1 of 1)

- [Mapping LiDAR and Camera Measurements in a Dual Top-View Grid Representation Tailored for Automated Vehicles](https://mlsanity.com/paper/2204.07887/) (2022)

## Abstract (arXiv)

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.