Probabilistic state estimation of dynamic objects with a moving mobile robot
Mobile service robots are designed to operate in dynamic and populated environments. To plan their missions and to perform them successfully, mobile robots need to keep track of relevant changes in the environment. For example, office delivery or cleaning robots must be able to estimate the state of doors or the position of waste-baskets in order to deal with the dynamics of the environment. In this paper we present a probabilistic technique for estimating the state of dynamic objects in the environment of a mobile robot. Our method matches real sensor measurements against expected measurements obtained by a sensor simulation to efficiently and accurately identify the most likely state of each object even if the robot is in motion. The probabilistic approach allows us to incorporate the robots uncertainty in its position into the state estimation process. The method has been implemented and tested on a real robot. We present different examples illustrating the efficiency and robustness of our approach.
probabilistic state estimation mobile service robots sensor interpretation