A Surface Adaptive First-Look Inspection Planner for Autonomous Remote Sensing of Open-Pit Mines

TL;DR

This paper introduces an adaptive inspection planning framework for autonomous remote sensing in open-pit mines, utilizing real-time 3D LiDAR data to optimize inspection paths amid changing mine-face geometries.

Contribution

It develops a novel online view-planning method that adapts initial inspection plans based on current mine-face morphology using real-time sensor data.

Findings

Effective in simulation within a mine environment

Validated through outdoor hardware experiments

Demonstrates adaptability to dynamic mine-face changes

Abstract

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