An Improved Extended Information Filter SLAM Algorithm Based on Omnidirectional VisionReport as inadecuate

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Journal of Applied Mathematics - Volume 2014 2014, Article ID 948505, 10 pages -

Research ArticleDepartment of Automation, Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, Ministry of Education of China, Shanghai 200240, China

Received 31 March 2014; Revised 28 May 2014; Accepted 18 June 2014; Published 20 July 2014

Academic Editor: Yantao Shen

Copyright © 2014 Jingchuan Wang and Weidong Chen. 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.


In the SLAM application, omnidirectional vision extracts wide scale information and more features from environments. Traditional algorithms bring enormous computational complexity to omnidirectional vision SLAM. An improved extended information filter SLAM algorithm based on omnidirectional vision is presented in this paper. Based on the analysis of structure a characteristics of the information matrix, this algorithm improves computational efficiency. Considering the characteristics of omnidirectional images, an improved sparsification rule is also proposed. The sparse observation information has been utilized and the strongest global correlation has been maintained. So the accuracy of the estimated result is ensured by using proper sparsification of the information matrix. Then, through the error analysis, the error caused by sparsification can be eliminated by a relocation method. The results of experiments show that this method makes full use of the characteristic of repeated observations for landmarks in omnidirectional vision and maintains great efficiency and high reliability in mapping and localization.

Author: Jingchuan Wang and Weidong Chen



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