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.

The fast growth of mobile autonomous machines from traditional equipment to unmanned autonomous vehicles has fueled the demand for accurate and reliable localization solutions in diverse application domains. Ultra Wide Band (UWB) technology has emerged as a promising candidate for addressing this need, offering high precision, immunity to multipath interference, and robust performance in challenging environments. In this comprehensive survey, we systematically explore UWB-based localization for mobile autonomous machines, spanning from fundamental principles to future trends. To the best of our knowledge, this review paper stands as the pioneer in systematically dissecting the algorithms of UWB-based localization for mobile autonomous machines, covering a spectrum from bottom-ranging schemes to advanced sensor fusion, error mitigation, and optimization techniques. By synthesizing existing knowledge, evaluating current methodologies, and highlighting future trends, this review aims to catalyze progress and innovation in the field, unlocking new opportunities for mobile autonomous machine applications across diverse industries and domains. Thus, it serves as a valuable resource for researchers, practitioners, and stakeholders interested in advancing the state-of-the-art UWB-based localization for mobile autonomous machines.