Lightweight Map Storage and Retrieval Method for Autonomous Navigation of UAVs in Large-Scale Cylindrical Spaces

Abstract

The inspection of autonomous Unmanned Aerial Vehicles (UAVs) in large cylindrical enclosed spaces has difficulties in positioning and processing massive global map data in real-time required for planning. Because such environments have the characteristics of darkness, GPS signal rejection, approximate cylinder, and large-scale environmental spaces. Therefore, we built a UAV using multi-line lidar and simultaneous localization and mapping (SLAM) in this environment to adapt to the former characteristics. In the face of the feature degradation in the height direction caused by the cylindrical-like features of the environment, the constraint information of the height sensor is added in lidar SLAM. And a map storage method called selective ordered indexed map (SOIM) is proposed for storing and real-time retrieval of huge amount of map data in large spaces required by path planning during autonomous inspection tasks. We conducted inspection experiment in a desulfurization tower of a thermal power plant to verify the function of designed system. The results prove the feasibility of our system and presented that the proposed SOIM saved 98.86% of storage space compared to the point cloud map in this environment, and the voxel search speed was 22.53% higher than that of octree search.