Recent approaches to appearance-based robot localization and mapping have relied on a topological representation of the environment and loop closing procedures to build correct maps. Because these solutions do not separate the problem of localization in mapped areas from the exploration of new locations, they require that loop-closing detection is performed continuously, inducing an extra computational burden on the robot. In this paper, we introduce a modified version of the Markov localization algorithm which avoids this condition by employing procedures that deflect when novel areas are being explored or already mapped areas are revisited.
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