In open-pit mining, holes are drilled into the surface of the excavation site and detonated with explosives to facilitate digging. These blast holes need to be inspected internally to assess subsurface material types and drill quality, in order to significantly reduce downstream material handling costs. Manual hole inspection is slow and expensive, limited in its ability to capture the geometric and geological characteristics of holes. This has been the motivation for 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 perception and navigation framework that provides streamlined blasthole seeking, tracking and accurate down-hole sensor positioning. To address challenges in the surface mining environment, where GPS and odometry data are noisy without RTK correction, we adopt a proximity-based adaptive navigation approach, enabling the vehicle to dynamically adjust its operations based on detected target availability and localisation accuracy. For perception, we process LiDAR data to extract the cone-shaped volume of drill-waste above ground, 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 projection parameters to preserve consistent hole image appearance. At the vicinity of the hole, we apply least squares circle fitting with non-maximum candidate suppression to achieve accurate hole detection and collision-free down-hole sensor placement. We demonstrate the effectiveness of our navigation and perception system in both high-fidelity simulation environments and on-site field trials. A demonstration video is available at https://www.youtube.com/watch?v=fRNbcBcaSqE.

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