Fault tolerant decentralized collaboration for simultaneous localization and prior map update with stable 2D features

Accurate and reliable localization is crucial for safe navigation of autonomous robots. In environments where GNSS performs poorly, robots can rely on stable 2D features stored on prior vector maps, such as OpenStreetMap, which are detected by on-board sensors. This paper proposes a fault tolerant decentralized collaborative method for online localization and map update. We focus on the case of indirect collaboration, where robots only collaborate through the observation of common landmarks. The method relies on the Schmidt–Kalman filter, which handles correlations between robots while minimizing the number of communications. A Kullback–Leibler Average data fusion is also employed. To ensure the integrity of the state estimation, a fault detection, isolation, and recovery approach is proposed, enabling the detection of sensor faults and the correction of map faults through collaboration. The method is evaluated using both simulation and experimental data. Results show that collaboration improves accuracy and consistency of localization and mapping, and improves the ability to detect faults. • Decentralized collaborative localization with map update using landmark observations. • Approach based on Schmidt–Kalman filter and Kullback–Leibler average. • Decentralized fault detection, isolation and recovery for sensor and map faults. • Simulation and experimental results with intelligent vehicles in urban environment.