Optimizing multi-robot localization with Extended Kalman Filter feedback and collaborative laser scan matching

Abstract: Localization is a critical aspect of robots and their industrial applications, with its major impact on navigation and planning. The goal of this thesis is to improve multi-robot localization by utilizing scan matching algorithms to calculate a corrected pose estimate using the robots' shared laser scan data. The current pose estimate relative to the map is used as the initial guess for the scan matching. This corrected pose is fused using several different localization configurations, such as an Extended Kalman Filter in combination with the Adaptive Monte Carlo Localization algorithm. Simulations showed that localization improved by resetting the Monte Carlo particle filter with the pose estimate generated by the collaborative scan matching. Further, in simulated scenarios, the collaborative scan matching implementation improved the accuracy of typical Monte Carlo Localization configurations. Furthermore, when filtering based on the number of reciprocal correspondences between the scan match output and the target scan, one could extract highly accurate pose estimates. When resetting the Monte Carlo Localization algorithm with the pose estimates, the localization algorithm could successfully recover from severe errors in the global positioning. In future work, additional testing needs to be done using these extracted pose estimates in a dedicated map-based multi-robot localization algorithm.