Probabilistic mapping of unexpected objects by a mobile robot

We present a technique for extending a given metric map of the environment by objects of a known type, where localization and perception of the robot is allowed to be uncertain. The advantage of our approach is that it allows the robot to estimate its own position in the given outline of the environment and thus to estimate the position of the objects not contained in the map. The method relies on partially observable Markov decision processes as well as on the Baum-Welch algorithm. It has been implemented and evaluated in several simulation experiments and also in a real-world sewage pipe network. The experimental results demonstrate that our approach can efficiently and accurately estimate the position of unexpected objects. Due to the probabilistic nature of the underlying techniques, our method can deal with noisy sensors as well as with large odometry errors which generally occur when deploying a robot in a sewerage pipe system