Autonomous exploration using UWB and LiDAR

In autonomous exploration, a robot navigates itself in an unknown environment while building a 2D map of the environment. This is typically done using a LiDAR sensor, which however is susceptible to error accumulation. To handle this issue, a UWB/LiDAR fusion SLAM is proposed, which can be decoupled into a localization problem and a mapping problem. For localization problem, we firstly apply extended Kalman filter (EKF) to localize all UWB beacons and then use particle filter (PF) to estimate the robot’s state based on the two on-board UWB nodes’ estimated locations. For mapping problem, we firstly fine-tune the robot’s state using a recursive adaptive-trust-region scan matcher, which is termed as RASM, and then construct the map based on the refined robot’s state. We also propose a method to correct UWB beacons’ locations using the robot’s refined location. Furthermore, the information obtained from the proposed fusion SLAM is utilized to sketch the region where the robot is going to explore next. That is, a where-to-explore strategy is proposed to guide the robot to the less-explored areas. Overall, the proposed exploration system is infrastructure-less and avoid mapping error to accumulate over time. Extensive experiments with comparisons to the state-of-the-art methods are conducted in two different environments: a cluttered workshop and a spacious garden in order to verify the effectiveness of our proposed strategy. The experimental tests are filmed and the video is available in the supplementary materials.