Distributed State Estimation for Mobile Robots in LiDAR Sensor Networks With Intermittent Inertial Measurements

The growing deployment of multiple robots in dynamic scenes and unstructured settings requires scalable and robust estimation techniques, particularly those that leverage distributed sensor networks (DSN). This paper introduces a distributed observer framework designed for state estimation and tracking of autonomous mobile robots in LiDAR sensor networks. The framework incorporates remote state estimation and intermittent onboard inertial data to improve tracking accuracy and scalability. It reduces communication overhead and minimizes computational burden on DSN through a reliable clustering approach, addressing the challenges posed by large data volumes. In addition, a 3D bounding box detection module is developed to accommodate various sensor configurations, enabling robust and efficient object tracking in unstructured environments. The consensus properties of the distributed state estimators is analyzed, and the asymptotic stability of the corresponding estimation error dynamics is formally established. The proposed framework is validated through extensive experimental studies involving occlusions in dynamic scenes, demonstrating high estimation accuracy, consistency, and computational efficiency across a broad range of operating conditions.