A Gaussian Process-Based Ground Segmentation for Sloped Terrains

TL;DR

This paper introduces a probabilistic Gaussian Process-based method for ground segmentation in LiDAR data that effectively models sloped and uneven terrains without requiring height constraints.

Contribution

It develops a novel Gaussian Process approach with a non-stationary kernel for accurate, constraint-free ground segmentation in complex terrains, using a Bayesian inference framework.

Findings

Outperforms existing Gaussian process-based methods in uneven scenes

Effectively models sloped and flat ground without height constraints

Handles ground-like obstacle points with a density parameter

Abstract

A Gaussian Process GP based ground segmentation method is proposed in this paper which is fully developed in a probabilistic framework. The proposed method tends to obtain a continuous realistic model of the ground. The LiDAR three-dimensional point cloud data is used as the sole source of the input data. The physical realities of the data are taken into account to properly classify sloped ground as well as the flat ones. Furthermore, unlike conventional ground segmentation methods, no height or distance constraints or limitations are required for the algorithm to be applied to take all the regarding physical behavior of the ground into account. Furthermore, a density-like parameter is defined to handle ground-like obstacle points in the ground candidate set. The non-stationary covariance kernel function is used for the Gaussian Process, by which Bayesian inference is applied using the maximum A Posteriori criterion. The log-marginal likelihood function is assumed to be a multi-task objective function, to represent a whole-frame unbiased view of the ground at each frame. Simulation results show the effectiveness of the proposed method even in an uneven, rough scene which outperforms similar Gaussian process-based ground segmentation methods.