Decentralized Collaborative Localization with Map Update using Schmidt-Kalman Filter

This paper presents a new decentralized approach for collaborative localization and map update relying on land-marks measurements performed by the robots themselves. The method uses a modified version of the Kalman filter, namely Schmidt Kalman filter that approaches the performance of the optimal centralized Kalman filter without the need to update each robot pose. To deal with data incest and limited communication, the computation of cross-covariance errors between robots must be well managed. Each robot individually updates its own map, the map fusion is performed by using the unweighted Kullback-Leibler Average to keep estimation consistency. The performance of the approach is evaluated in a simulation environment where robots are equipped with odometry and a lidar for exteroceptive perception. The results show that collaboration improves the localization of the robots and the estimation of the map while maintaining consistency.