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This paper presents a fully distributed relative localization method for multirobot systems in GPS-denied, communication-limited environments by fusing onboard UWB ranging and LiDAR-based object detection. By constructing UWB and LiDAR connection graphs and formulating identification and localization as a subgraph matching problem, the approach robustly handles LiDAR occlusions through a false match identification mechanism. Experimental results demonstrate the system's capability to achieve satisfactory positioning accuracy with limited inter-robot communication.
A novel distributed subgraph matching framework leverages UWB ranging and LiDAR object detection to achieve robust multirobot relative localization even with significant communication constraints and sensor occlusions.
Relative localization is crucial for a multirobot system to collaboratively perform tasks, such as exploration and formation. However, this is highly challenging for homogeneous robots with similar appearance in GPS-denied and communication-limited environments. In this article, we propose a fully distributed relative position estimation approach for a team of robots based on onboard ultra-wideband (UWB) and light detection and ranging (LiDAR) sensors, in which LiDAR is utilized to obtain the position of anonymous objects in line-of-sight (LOS), and UWB is used for ranging between robots. We construct two graphs, namely UWB connection graph and LiDAR connection graph, to represent the spatial relationship among objects (robots and obstacles) based on UWB and LiDAR measurements. Identification and relative position estimation are formulated as a common subgraph matching problem. A falsely matched robot identification approach is designed to recognize the falsely matched results caused by obstacle blockage in LiDAR field of view. These robots are then localized by leveraging the well-matched robots and the UWB ranging measurements in the UWB connection graph. We conducted experiments to evaluate the performance of our approach. The results show that the proposed approach is capable of achieving satisfactory positioning accuracy for a team of robots in a distributed manner with only exchanging limited information.