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

翻译：本研究提出了一种用于活跃露天矿遥感任务的自主巡检框架。具体而言，本工作的贡献集中于开发一种方法学：通过在线视图规划器，利用由操作员定义的初始近似巡检计划，预测能够适应因路线开采活动导致的当前矿面形态变化的巡检路径。所提出的巡检框架结合瞬时三维激光雷达与定位测量数据，并利用建模的传感器视场进行视图规划，以满足期望的观测与摄影测量条件。该框架的有效性已通过在费灵-布鲁克露天矿环境中的仿真及基于硬件的户外实验验证。展示所提工作性能的视频可见于：https://youtu.be/uWWbDfoBvFc