Vision-based semantic unscented FastSLAM for indoor service robot

Abstract

This paper proposes a vision-based Semantic Unscented FastSLAM (UFastSLAM) algorithm for mobile service robot combining the semantic relationship and the Unscented FastSLAM. The landmark positions and the semantic relationships among landmarks are detected by a binocular vision. Then 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 locations of the landmarks and robot pose even when the encoder inherits large cumulative 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 surveillance than Unscented FastSLAM.