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

TL;DR

GSeg3D introduces a high-precision, grid-based ground segmentation algorithm for LiDAR point clouds, enhancing safety-critical perception in autonomous driving and robotics by reducing false detections.

Contribution

The paper presents a novel grid-based ground segmentation method that achieves high precision suitable for safety-critical applications in autonomous systems.

Findings

Achieves high segmentation accuracy in LiDAR data

Reduces false positives in obstacle detection

Supports real-time processing in autonomous vehicles

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.