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.

In this paper, we address the problem of building a grid map using cheap sonar sensors, that is, the problem of using erroneous sensors when seeking to model an environment as accurately as possible. We rely on the inconsistency of information among sonar measurements and the sound pressure of the waves from the sonar sensors to develop a new method of detecting incorrect sonar readings, called the conflict evaluation with sound pressure (CEsp). To fuse the correct measurements into a map, we start with the maximum likelihood approach due to its ability to manage the angular uncertainty of sonar sensors. However, since this approach suffers from heavy computational complexity, we convert it to a light logic problem called the maximum approximated likelihood (MAL) approach. Integrating the MAL approach with the CEsp method results in the conflict-evaluated maximum approximated likelihood (CEMAL) approach. The CEMAL approach generates a very accurate map that is close to the map that would be built by accurate laser sensors, and does not require adjustment of parameters for various environments.

We present robust localization algorithm using particle filter for mobile robots. The blueprint of building that cannot completely represent the real environment or a simple map of environment can be used in this work. With this incomplete information, it will not be easy to estimate the pose of robots because un-modeled obstacles will make the sensor observation inconsistent. Proposed algorithm provides robustness in such a dynamic environment because the effect of corrupted sensor observations can be suppressed by a selective update or a sampling from non-corrupted window. Results of experiments and simulations show the robustness of proposed algorithm.