Blast Hole Seeking and Dipping: Navigation and Perception Framework in a Mine Site Inspection Robot

In open-pit mining, holes are drilled into the surface of the excavation site and are then detonated with explosives to facilitate digging. These blast holes need to be inspected internally for quality assurance, as well as for operational and geological reasons. Manual hole inspection is slow and expensive, limited in its ability to capture the geometric and geological characteristics of holes. This is the motivation behind the development of our autonomous mine site inspection robot—“DIPPeR”. In this paper, the automation aspect of the project is explained. We present a robust navigation and perception framework that provides streamlined blasthole detection, tracking, and precise down-hole sensor insertion during repetitive inspection tasks. To mitigate the effects of noisy GPS and odometry data typical of surface mining environments, we employ a proximity-based adaptive navigation system that enables the vehicle to dynamically adjust its operations according to target detectability and localisation accuracy. For perception, we process LiDAR data to extract the cone-shaped volume of drill waste above ground, and then project the 3D cone points into a virtual depth image to form accurate 2D segmentation of hole regions. To ensure continuous target tracking as the robot approaches the goal, our system automatically adjusts the projection parameters to ensure consistent appearance of the hole in the image. In the vicinity of the hole, we apply least squares circle fitting combined with non-maximum candidate suppression to achieve accurate hole localisation and collision-free down-hole sensor insertion. We demonstrate the effectiveness and robustness of our framework through dedicated perception and navigation feature tests, as well as streamlined mission trials conducted in high-fidelity simulations and real mine-site field experiments.