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Assistant robots have received special attention from the research community in the last years. One of the main applications of these robots is to perform care tasks in indoor environments such as houses, nursing homes or hospitals, and therefore they need to be able to navigate robustly for long periods of time. This paper focuses on the navigation system of SIRA, a robotic assistant for elderly and/or blind people based on a Partially Observable Markov Decision Process (POMDP) to global localize the robot and to direct its goal-oriented actions. The main novel feature of our approach is that it combines sonar and visual information in a natural way to produce state transitions and observations in the framework of Markov Decision Processes. Besides this multisensorial fusion, a two-level layered planning architecture that combines several planning objectives (such as guiding to a goal room and reducing locational uncertainty) improves the robustness of the navigation system, as it's shown in our experiments with SIRA navigating corridors.