提出高精度地面分割算法，提升自动驾驶安全感知能力。
GSeg3D: A High-Precision Grid-Based Algorithm for Safety-Critical Ground Segmentation in LiDAR Point Clouds
- 基于网格的高效算法，精准区分地面与非地面点云
- 专为高安全性场景设计，降低误检率保障决策可靠
- 适用于自动驾驶与机器人实时地面识别
点云中的地面分割是将地面点与非地面点分离的过程，是自动驾驶和机器人感知的基础任务。在安全关键环境中，精确检测障碍物和可行驶表面对系统安全至关重要。现有方法难以满足高精度要求，常导致误检，影响决策可靠性。本文提出一种面向安全关键场景的地面分割方法，旨在实现持续高精度分割，支持自动驾驶车辆和机器人在真实复杂环境中的可靠运行。
原文摘要 · Abstract (English)
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.
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