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.
Highlights
Visual simultaneous localization and mapping (SLAM) uses the cameras as the only exteroceptive sensors to recover a representation of the environment and achieve localization of the robot complemented with information from the proprioceptive sensors with the aim of increasing accuracy and robustness
Semantic unscented FastSLAM partitions the SLAM posterior into a localization problem and independent landmark position estimation problem conditioned on the robot pose estimate and the semantic metric relationships between the landmarks as follows: p (Xt, mt, rt | zt,semantic, ut) = p (Xt | zt,semantic, ut)
This paper has proposed a vision-based Semantic Unscented FastSLAM for mobile robot
Summary
Visual simultaneous localization and mapping (SLAM) uses the cameras as the only exteroceptive sensors to recover a representation of the environment and achieve localization of the robot complemented with information from the proprioceptive sensors with the aim of increasing accuracy and robustness. Thrun and Buecken combined the grid based and topological based methods to map indoor robot environments [21] Such hybrid algorithms took advantage of the local metric grids for enhanced local planning while avoiding the computation of a complete global grid map. These maps are very limited in describing the environment other than distinguishing between occupied and empty areas. The main contribution of this paper includes a novel Semantic Unscented FastSLAM algorithm to improve accuracy of localization and mapping while maintaining the sparse map for real-time implementation. The rest of the paper is organized as follows: Section 2 describes the semantic topological metric map and observation model.
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