Abstract
This paper proposes a vision-based Semantic Unscented FastSLAM (UFastSLAM) algorithm for mobile robot combing the semantic relationship and the unscented FastSLAM. The landmarks are detected by a binocular vision, and the semantic observation model can be created by transforming the semantic relationships into the semantic metric map. Semantic Unscented FastSLAM can be used to update the localization of the landmarks and robot pose even when the encoders inherits large accummative errors that may not be corrected by the loop closure detection of the vision system Experiments have been carried out to demonstrate that the Semantic Unscented FastSLAM algorithm can achieve much better performance in indoor autonomous survalience than Unscented FastSLAM.
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