智能规划矿坑表面巡检路径，实时适应开采变化
A Surface Adaptive First-Look Inspection Planner for Autonomous Remote Sensing of Open-Pit Mines
- 基于初始计划与实时激光扫描，动态生成巡检路径
- 在模拟和实地试验中均实现精准覆盖与高质量成像
- 适合需要自主巡检的露天矿、无人值守监测场景
本文提出一种面向活跃露天矿远程感知任务的自主巡检框架。核心贡献在于：利用初始人工定义的巡检方案，由在线视图规划器根据实时三维激光雷达与定位数据，预测可自适应矿坑面形态变化（由采掘活动引起）的巡检路径。该框架结合瞬时3D LiDAR与定位测量，以及建模的传感器视场范围，确保满足期望的观测角度与摄影测量条件。通过在Feiring-Bruk露天矿环境中的仿真验证及硬件实测户外试验，证明了该方法的有效性。相关演示视频见：https://youtu.be/uWWbDfoBvFc
原文摘要 · Abstract (English)
In this work, we present an autonomous inspection framework for remote sensing tasks in active open-pit mines. Specifically, the contributions are focused towards developing a methodology where an initial approximate operator-defined inspection plan is exploited by an online view-planner to predict an inspection path that can adapt to changes in the current mine-face morphology caused by route mining activities. The proposed inspection framework leverages instantaneous 3D LiDAR and localization measurements coupled with modelled sensor footprint for view-planning satisfying desired viewing and photogrammetric conditions. The efficacy of the proposed framework has been demonstrated through simulation in Feiring-Bruk open-pit mine environment and hardware-based outdoor experimental trials. The video showcasing the performance of the proposed work can be found here: https://youtu.be/uWWbDfoBvFc
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