Research on an autonomously tightly integrated positioning method for UAV in sparse-feature indoor environment

Although Light Detection and Ranging (LiDAR) based Simultaneous Localization and Mapping (SLAM) has been widely used for ground robot's autonomous localization, there is difficulty in localizing an unmanned aerial vehicle (UAV) due to variation in altitude and motion dynamics. Especially, the positioning error will increase speedily when UAV fly in the environment with sparse features. In this paper, a Micro Electro Mechanical Systems Inertial Measurement Unit (MEMS-IMU) aided SLAM method in sparse indoor environments with few features will be proposed, namely LiDAR/MEMS-IMU tightly integrated positioning method. The introduction of MEMS-IMU as the state prediction in the SLAM, feature searching range can be reduced and the positioning accuracy is improved drastically through this algorithm. Our LiDAR-based SLAM experiments conducted in a corridor with sparse features and results showed that the proposed LiDAR/MEMS-IMU tightly integrated positioning algorithm had 10 times better positioning accuracy comparing with the traditional algorithm.