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| 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 |
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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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Received: 03 June 2008
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[1] Smith R, Cheesman P. On the Representation of Spatial Uncertainty. International Journal of Robotics Research, 1987, 5(4): 56-68
[2] Durrant-Whyte H F. Uncertain Geometry in Robotics. IEEE Trans on Robotics and Automation, 1988, 4(1): 23-31
[3] Smith R, Self M, Cheeseman P. Autonomous Robot Vehicles. New York, USA: Springer-Verlag, 1990: 167-193
[4] Thrun S, Fox D, Burgard W. A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots. Machine Learning, 1998, 31(1/2/3): 29-53
[5] Fox D, Burgard W, Kruppa H, et al. A Probabilistic Approach to Collaborative Multi-Robot Localization. Autonomous Robots, 2000, 8(3): 325-344
[6] Guivant J E, Nebot E M. Optimization of the Simultaneous Localization and Map-Building Algorithm for Real-Time Implementation. IEEE Trans on Robotics and Automation, 2001, 17(3): 242-257
[7] Doucet A, de Freitas J, Murphy K, et al. Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks // Proc of the 16th Conference on Uncertainty in Artificial Intelligence. Stanford, USA, 2000: 176-183
[8] Montemerlo M, Koller S T D, Wegbreit B. FastSLAM 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges // Proc of the 18th International Conference on Artificial Intelligence. Acapulco, Mexico, 2003: 1151-1156
[9] Bailey T, Nieto J, Nebot E. Consistency of the FastSLAM Algorithm // Proc of the IEEE International Conference on Robotics and Automation. Orlando, USA, 2006: 424-429
[10] Klaas M, de Freitas N, Doucet A. Toward Practical n2 Monte Carlo: The Marginal Particle Filter // Proc of the 21st Conference on Uncertainty in Artificial Intelligence. Edinburgh, UK, 2005: 308-315
[11] Martinez-Cantin R, de Freitas N, Castellanos J A. Analysis of Particle Methods for Simultaneous Robot Localization and Mapping and a New Algorithm: Marginal-SLAM // Proc of the IEEE International Conference on Robotics and Automation. Rome, Italy, 2007: 2415-2420
[12] Julier S J, Uhlmann J K. A New Extension of the Kalman Filter to Nonlinear Systems // Proc of the 11th International Symposium on Aerospace/Defense Sensing. Orlando, USA, 1997: 182-193
[13] var der Merwe R, de Freitas N, Doucet A, et al. The Unscented Particle Filter. Portland, USA: Oregon Graduate Institute, 2000
[14] Kim C, Sakthivel R, Chung W K. Unscented FastSLAM: A Robust Algorithm for the Simultaneous Localization and Mapping Problem // Proc of the IEEE International Conference on Robotics and Automation. Piscataway, USA, 2007: 2439-2445
[15] Yang C, Duraiswami R, Gumerov N A, et al. Improved Fast Gauss Transform and Efficient Kernel Density Estimation // Proc of the 9th IEEE International Conference on Computer Vision. Washington, USA, 2003, Ⅰ: 464-471 |
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2009, Vol. 22 
