Online mapping with a mobile robot in dynamic and unknown environments

In this paper, we address the problem of mapping dynamic and unknown environments. The static and moving objects are modelled as the components in a Gaussian mixture model (GMM). By recursive learning of GMM, the components corresponding to the static objects will have larger weights while the components corresponding to the moving objects will have smaller weights. At each time step, a number of components with the largest weights are adaptively selected as the background map and the new observations which do not match with the background map are classified as the foreground map. In addition, based on a Bayesian factorisation of simultaneous localisation and mapping (SLAM) problem, we present an online algorithm for SLAM with GMM learning. Our contributions are employing GMM learning approach to model the dynamic environment with detection of moving objects and jointing the GMM learning with SLAM in unknown environment. Consequently, an online approach for mapping with a mobile robot in dynamic and unknown environments is presented. Some simulation results indicate that our approach is feasible.

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