A semantic knowledge database-based localization method for UAV inspection in perceptual-degraded underground mine

Abstract In recent years, unmanned aerial vehicles (UAVs) have been applied in underground mine inspection and other similar works depending on their versatility and mobility. However, accurate localization of UAVs in perceptually degraded mines is full of challenges due to the harsh light conditions and similar roadway structures. Due to the unique characteristics of the underground mines, this paper proposes a semantic knowledge database-based localization method for UAVs. By minimizing the spatial point-to-edge distance and point-to-plane distance, the relative pose constraint factor between keyframes is designed for UAV continuous pose estimation. To reduce the accumulated localization errors during the long-distance flight in a perceptual-degraded mine, a semantic knowledge database is established by segmenting the intersection point cloud from the prior map of the mine. The topological feature of the current keyframe is detected in real time during the UAV flight. The intersection position constraint factor is constructed by comparing the similarity between the topological feature of the current keyframe and the intersections in the semantic knowledge database. Combining the relative pose constraint factor of LiDAR keyframes and the intersection position constraint factor, the optimization model of the UAV pose factor graph is established to estimate UAV flight pose and eliminate the cumulative error. Two UAV localization experiments conducted on the simulated large-scale Edgar Mine and a mine-like indoor corridor indicate that the proposed UAV localization method can realize accurate localization during long-distance flight in degraded mines.