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In urban search and rescue scenarios, typical applications of robots include autonomous exploration of possibly dangerous sites, and the recognition of victims and other objects of interest. In complex scenarios, relying on only one type of sensor is often misleading, while using complementary sensors frequently helps improving the performance. To that end, the authors propose a probabilistic world model that leverages information from heterogeneous sensors and integrates semantic attributes. This method of reasoning about complementary information is shown to be advantageous, yielding increased reliability compared to considering all sensors separately.
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