GSeg3D: A High-Precision Grid-Based Algorithm for Safety-Critical Ground Segmentation in LiDAR Point Clouds

TL;DR

This work presents a ground segmentation approach designed to deliver consistently high precision, supporting the stringent requirements of autonomous vehicles and robotic systems operating in real-world, safety-critical scenarios.

Abstract

Ground segmentation in point cloud data is the process of separating ground points from non-ground points. This task is fundamental for perception in autonomous driving and robotics, where safety and reliable operation depend on the precise detection of obstacles and navigable surfaces. Existing methods often fall short of the high precision required in safety-critical environments, leading to false detections that can compromise decision-making. In this work, we present a ground segmentation approach designed to deliver consistently high precision, supporting the stringent requirements of autonomous vehicles and robotic systems operating in real-world, safety-critical scenarios.

Figures (6)

• Figure 1: GSeg3D: The point cloud is segmented into ground points (magenta) and non-ground points, which are color-coded according to their height to visualize vertical structures.
• Figure 2: Each phase consists of a four-step process. The figure displays the raw point cloud (in white), the detected ground points (highlighted in magenta), and the non-ground points, which are color-coded according to their height along the positive z-axis.
• Figure 3: Phase I uses grid cells with large height to capture tall structures as non-ground points. The coarse ground points from Phase I are subsequently processed in Phase II using grid cells with small height.
• Figure 4: Correction of over-segmentation of ground points based on dual-phase segmentation.
• Figure 5: Points (black) assigned to a grid cell with dominant eigenvectors (green) are shown. The cell is classified as Line (a), Planar (b), or Non-Planar (c) based on the local eigen classification.