用双激光雷达提升叉车在户外环境的语义感知能力
LiDAR Based Semantic Perception for Forklifts in Outdoor Environments
- 采用前视与俯视双激光雷达融合感知
- 实现在动态场景中对障碍物和地面的高精度分割
- 适合需要安全自主导航的智能叉车应用
本研究提出一种面向复杂户外环境作业的自主叉车的新型激光雷达语义分割框架。核心是融合前向与俯角双激光雷达系统，实现对工业物料搬运场景的全面感知。利用两路高分辨率3D点云数据，该方法采用轻量且鲁棒的策略，将点云分割为安全关键实例类别（如行人、车辆、叉车）及环境类别（如可行驶地面、车道、建筑）。实验验证表明，该方法在满足严格运行时要求的前提下实现了高分割精度，适用于动态仓库与堆场环境中具备安全意识的全自主叉车导航。
原文摘要 · Abstract (English)
In this study, we present a novel LiDAR-based semantic segmentation framework tailored for autonomous forklifts operating in complex outdoor environments. Central to our approach is the integration of a dual LiDAR system, which combines forward-facing and downward-angled LiDAR sensors to enable comprehensive scene understanding, specifically tailored for industrial material handling tasks. The dual configuration improves the detection and segmentation of dynamic and static obstacles with high spatial precision. Using high-resolution 3D point clouds captured from two sensors, our method employs a lightweight yet robust approach that segments the point clouds into safety-critical instance classes such as pedestrians, vehicles, and forklifts, as well as environmental classes such as driveable ground, lanes, and buildings. Experimental validation demonstrates that our approach achieves high segmentation accuracy while satisfying strict runtime requirements, establishing its viability for safety-aware, fully autonomous forklift navigation in dynamic warehouse and yard environments.
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