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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 149206, 10 pages
http://dx.doi.org/10.1155/2015/149206
Research Article

Vision-Based Semantic Unscented FastSLAM for Indoor Service Robot

1University Town of Shenzhen, HIT Campus, Nanshan District, Shenzhen 518055, China
2Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China

Received 19 December 2014; Accepted 8 June 2015

Academic Editor: Shaoping Bai

Copyright © 2015 Xiaorui Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.