Mobile robot mapping in populated environments

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Abstract
The problem of learning maps with mobile robots has received considerable attention over the past years. Most of the approaches, however, assume that the environment is static during the data-acquisition phase. In this paper we consider the problem of creating maps with mobile robots in populated environments. Our approach uses a probabilistic method to track multiple people and to incorporate the estimates of the tracking technique into the mapping process. The resulting maps are more accurate since the number of spurious objects is reduced and since the robustness of range registration is improved. Our approach has been implemented and tested on real robots in indoor and outdoor scenarios. We present several experiments illustrating the capabilities of our approach to generate accurate two- and three-dimensional maps.

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