Abstract: Unmanned driving technology has enormous advantages in improving efficiency, saving costs and reducing safety hazards. In the current implementation of localization and navigation solutions in underground environments, there are problems of implementation difficulties, high costs, and time-consuming construction of maps. In order to solve the above problems, a localization and navigation method for underground mine autonomous driving based on local geometric topology map is proposed. A local geometric topology map has been designed. The main structure of the underground environment road network is represented by a topology map. The map defines roadways (sides) and intersections (nodes), and stores a local geometric map built around the node in each node to achieve precise positioning at the node. A localization method based on local geometric topology map is proposed, which uses a LiDAR-based intersection detection algorithm and intersection localization algorithm for global vehicle localization. A trajectory-following algorithm based on adaptive model predictive control (MPC) has been designed to ensure the path-tracking precision of vehicles turning at high curvature intersections. A simulation environment and vehicle simulation model for underground mines are constructed by using a 3D physical simulation platform. The simulation results show that this method can achieve underground mine autonomous driving localization and navigation functions. The positioning errors are within 0.2 m at various types of intersections, meeting the positioning localization precision requirements of autonomous driving. Throughout the entire driving process, the vehicle maintains a relatively stable driving state and a small tracking error. Compared with the current localization and navigation methods that rely on technologies such as 5G and UWB, this method only relies on two types of vehicle sensors: LiDAR and inertial measurement unit. It has great advantages in controlling equipment costs.