Unmanned mining is one of the most effective methods to solve mine safety and low efficiency. However, it is the key to accurate localization and mapping for underground mining environment. A novel graph simultaneous localization and mapping (SLAM) optimization method is proposed, which is based on Generalized Iterative Closest Point (GICP) three-dimensional (3D) point cloud registration between consecutive frames, between consecutive key frames and between loop frames, and is constrained by roadway plane and loop. GICP-based 3D point cloud registration between consecutive frames and consecutive key frames is first combined to optimize laser odometer constraints without other sensors such as inertial measurement unit (IMU). According to the characteristics of the roadway, the innovative extraction of the roadway plane as the node constraint of pose graph SLAM, in addition to automatic removing the noise point cloud to further improve the consistency of the underground roadway map. A lightweight and efficient loop detection and optimization based on rules and GICP is designed. Finally, the proposed method was evaluated in four scenes (such as the underground mine laboratory), and compared with the existing 3D laser SLAM method (such as Lidar Odometry and Mapping (LOAM)). The results show that the algorithm could realize low drift localization and point cloud map construction. This method provides technical support for localization and navigation of underground mining environment.

Modeling ore body in 3D is the basis of digital intelligent mining. However, most existing three-dimensional mining software uses the contour approach that requires too much man–machine interaction and difficult partial updating. Moreover, accounting for uncertainty and low geometric quality picking is very difficult in the direct contour approach. Therefore, an implicit modeling approach to automatically build the three-dimensional model for ore body by means of spatial interpolation directly based on the geological borehole data with Hermite radial basis function (HRBF) algorithm as the core is proposed. Furthermore, in order to solve the problems of weak continuity of models due to the long-distance original boreholes as well as the boundary-point normal solution error, the densification of original borehole data with the virtual borehole as well as the calculation of point-cloud normal direction based on the adjacent hole-drilling method is proposed. The verification of two mine engineering projects and comparison with the explicit modeling results show that this approach could realize the automatic building of three-dimensional models for the ore body with high geometric quality, timely update and accurate results.

During the operation of open-pit mining, the loading position of a haulage truck often changes, bringing a new challenge concerning how to plan an optimal truck transportation path considering the terrain factors. This paper proposes a path planning method based on a high-precision digital map. It contains two parts: (1) constructing a high-precision digital map of the cutting zone and (2) planning the optimal path based on the modified Hybrid A* algorithm. Firstly, we process the high-precision map based on different terrain feature factors to generate the obstacle cost map and surface roughness cost map of the cutting zone. Then, we fuse the two cost maps to generate the final cost map for path planning. Finally, we incorporate the contact cost between tire and ground to improve the node extension and path smoothing part of the Hybrid A* algorithm and further enhance the algorithm’s capability of avoiding the roughness. We use real elevation data with different terrain resolutions to perform random tests and the results show that, compared with the path without considering the terrain factors, the total transportation cost of the optimal path is reduced by 10%–20%. Moreover, the methods demonstrate robustness.