An AMPF and FastSLAM Based Compositive SLAM Algorithm
ZHOU Wu1,2, ZHAO Chun-Xia1, ZHANG Hao-Feng1
1.College of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094 2.College of Vocational Technology, Zhejiang Normal University, Jinhua 321000
Abstract An AMPF and FastSLAM Based Compositive SLAM algorithm is presented to improve the performance of samples and increase the estimation accuracy. The auxiliary marginal particle filter (AMPF) is combined with the FastSLAM framework. In the proposed algorithm, the robot pose is estimated by AMPF, and the proposal pose distribution of each particle is estimated by UKF. Appropriate sampling strategy and particle data structure are designed to be compatible with both AMPF and FastSLAM framework. The map is estimated recursively by EKF in the FastSLAM framework. Experimental results indicate that the samples' performance of the proposed algorithm is better than that of FastSLAM 2.0, and the estimation accuracy of the proposed algorithm is higher than that of FastSLAM 2.0. Moreover, the estimation accuracy of the proposed algorithm is good with few samples. Therefore, it is feasible to improve the computational efficiency by reducing the number of samples.
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2009, Vol. 22 
