Appearance-based concurrent map building and localization using a multi-hypotheses tracker

The main drawback of appearance-based robot localization with respect to landmark-based one is that it requires a map including images taken at known positions in the area where the robot is expected to move. In this paper, we describe a concurrent map-building and localization (CML) system developed within the appearance-base robot localization paradigm. This allows us to combine the good features of appearance-base localization, such as simple sensor processing or robustness, without having to deal with its inconveniences. In our CML system, both the robot´s position and the map are represented using Gaussian mixtures. Using this kind of representation, we can deal with the global localization problem while being efficient both in memory and in execution time.