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| An Improved SLAM Algorithm with Sparse Extended Information Filters |
| GUO Jian-Hui, ZHAO Chun-Xia |
| College of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094 |
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Abstract How to achieve a sparse information matrix exactly is a key issue in sparse extended information filter (SEIF) simultaneous localization and map building (SLAM). A sparsification rule is put forward based on the deep analysis of correlation. The rule can utilize observation information of sparsification time, observe the correlation globally and reserve the features with the strongest correlation. The precision and consistency of the algorithm are improved without an increase of computational burden. Results of Monte-Carlo simulation experiments indicate the validity of the improved algorithm.
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Received: 27 August 2007
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2009, Vol. 22 
