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| An Improved Particle Filter SLAM Algorithm |
| WANG Xiao-Hua,YANG Xing-Fang |
| College of Electronics and Information,Xi′an Polytechnic University,Xi′an 710048 |
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Abstract The estimation accuracy of the conventional particle filter algorithm is low because the historical information is not fully utilized. Combining the high estimation accuracy of exactly sparse delayed-state filter(ESDF) and the high efficiency of exactly sparse extended information filter(ESEIF),an improved particle filter SLAM algorithm is proposed. In this algorithm,the information matrix of ESDF,maintaining the historical relationship of robot pose and characteristics,improves the accuracy of the estimate,and ESEIF overcomes the defects of robot rotational state and characteristics density.Results of both emulational and factual experiments show that the proposed algorithm is valid and feasible.
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Received: 16 October 2012
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2013, Vol. 26 
