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.

翻译：点云数据中的地面分割是将地面点与非地面点分离的过程。该任务是自动驾驶与机器人感知的基础，其安全与可靠运行依赖于对障碍物与可通行表面的精确检测。现有方法通常难以满足安全关键环境所需的高精度要求，导致可能影响决策的误检测。本工作提出一种地面分割方法，旨在持续提供高精度，以支持在现实世界安全关键场景中运行的自动驾驶车辆与机器人系统的严格要求。