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

TL;DR

This paper introduces a fast, robust method for estimating ground surfaces from LIDAR data on autonomous vehicles, utilizing Uniform B-Splines and efficient linear optimization for real-time application.

Contribution

It presents a novel approach that models the ground as a Uniform B-Spline and formulates the estimation as a linear, robust least squares problem for improved efficiency and robustness.

Findings

Effective ground classification on SemanticKITTI dataset

Real-time performance demonstrated on a research vehicle

Robustness to measurement density variations

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 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.