Robust RBPF-SLAM using sonar sensors in non-static environments

In this paper, we present a robust RBPF-SLAM algorithm for mobile robots in non-static environments. We propose an approach for sampling particles from multiple ancestor sets, not from just one prior set. This sampling method increases the robustness of SLAM algorithm, because some particles can be updated by only observations consistent with the map, even if observation at certain time step is corrupted by environmental changes. Corrupted observations are filtered out from recursive Bayesian update process by the proposed sampling method. We also present an intermediate path estimation method to use abandoned sensor information reflected from relocated objects for map update. The map can represent the changed configuration of non-static environment by the stored sensor information and the estimated path. Results of simulations and experiments in non-static environments show the robustness of proposed RBPF-SLAM algorithm using sonar sensors.