Square root central difference-based FastSLAM approach improved by differential evolution
Square root central difference-based FastSLAM approach improved by differential evolution
This study presents a new approach to improve the performance of FastSLAM. The aim of the study is to obtain a more robust algorithm for FastSLAM applications by using a Kalman filter that uses Stirling s polynomial interpolation formula. In this paper, some new improvements have been proposed; the first approach is the square root central difference Kalman filter-based FastSLAM, called SRCD-FastSLAM. In this method, autonomous vehicle (or robot) position, landmarks position estimations, and importance weight calculations of the particle filter are provided by the SRCD-Kalman filter. The second approach is an improved version of the SRCD-FastSLAM in which particles are improved by a differential evolution (DE) algorithm for reducing the risk of the particle depletion problem. Simulation results are given as a comparison of FastSLAM II, unscented (U)-FastSLAM, SRCD-Kalman filter-aided FastSLAM, SRCD particle filter-based FastSLAM, SRCD-FastSLAM, and DE-SRCD-FastSLAM. The results show that SRCDbased FastSLAM approaches accurately compute mean and precise uncertainty of the robot position in comparison with FastSLAM II and U-FastSLAM methods. However, the best results are obtained by DE-SRCD-FastSLAM, which provides significantly more accurate and robust estimation with the help of DE with fewer particles. Moreover, consistency of the DE-SRCD-FastSLAM is more prolonged than that of FastSLAM II, U-FastSLAM, and SRCD-FastSLAM.
___
- [1] Durrant-Whyte H, Bailey T. Simultaneous localization and mapping: Part I. IEEE Robot Autom Mag 2006; 2: 99108.
- [2] Bailey T, Durrant-Whyte H. Simultaneous localization and mapping: Part II. IEEE Robot Autom Mag 2006; 3: 108117.
- [3] Montemerlo M, Thrun S, Koller D, Wegbreit B. FastSLAM: A factored solution to the simultaneous localization and mapping problem. In: Proceedings of the Eighteenth National Conference on Artificial Intelligence; 2002; Menlo Park, CA, USA. Palo Alto, CA, USA: AAAI. pp. 593598.
- [4] Montemerlo M, Thrun S, Koller D, Wegbreit B. FastSLAM2.0: An improved particle ?ltering algorithm for simultaneous localization and mapping that provably converges. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence; 2003; Acapulco, Mexico. pp. 11511156.
- [5] Kim C, Sakthivel R, Chung WK. Unscented FastSLAM: A robust algorithm for simultaneous localization and mapping problem. In: Proceedings of the IEEE International Conference on Robotics & Automation; 2007; Rome, Italy. New York, NY, USA: IEEE. pp. 24392445.
- [6] Kim C, Sakthivel R, Chung WK. Unscented FastSLAM: a robust and efficient solution to the SLAM problem. IEEE T Robot 2008; 24: 808820.
- [7] Havangi R, Teshnehlab M, Nekoui MA. A neuro-fuzzy multi swarm FastSLAM framework. Journal of Computing 2010; 3: 8394.
- [8] Song Y, Li Q, Kang Y, Yan D. Square root cubature FastSLAM algorithm for mobile robot simultaneous localization and mapping. In: International Conference on Mechatronics and Automation; 58 August 2012; Chengdu, China. New York, NY, USA: IEEE. pp. 11621167.
- [9] Wang X, Zhang H. A UPF-UKF framework for SLAM. In: Proceedings of the IEEE International Conference on Robotics and Automation; 2007; Rome, Italy. New York, NY, USA: IEEE. pp. 16641669.
- [10] Dongbo L, Guorong L, Miaohua Y. An improved FastSLAM framework based on particle swarm optimization and unscented particle filter. Journal of Computational Information Systems 2012; 7: 28592866.
- [11] Murphy K. Bayesian map learning in dynamic environments. In: NIPS Proceedings; 1999; Cambridge, MA, USA. pp. 10151021.
- [12] Montemerlo M, Thrun S. Simultaneous localization and mapping with unknown data association using FastSLAM. In: IEEE International Conference on Robotics and Automation; 2003; Piscataway, NJ, USA. New York, NY, USA:IEEE. pp. 19851991.
- [13] Bailey T, Nieto J, Nebot E. Consistency of the FastSLAM algorithm. In: Proceedings of the IEEE International Conference on Robotics and Automation; 2006; Orlando, FL, USA. New York, NY, USA: IEEE. pp. 424429.
- [14] Bailey T, Nieto J, Guivant J, Stevens M, Nebot E. Consistency of the EKF-SLAM algorithm. In Proceedings of the IEEE/RSJ International Conference of Intelligent Robots Systems; 2006; Beijing, China. New York, NY, USA: IEEE. pp. 35623568.
- [15] Huang S, Dissanayake G. Convergence and consistency analysis for extended Kalman filter based SLAM. IEEE T Robot 2007; 5: 10361049.
- [16] Julier S. The scaled unscented transformation. In: Proceedings of the American Control Conference; 2002. New York, NY, USA: IEEE. pp. 45554559.
- [17] Norgaard M, Poulsen N, Ravn O. New developments in state estimation for nonlinear systems. Automatica 2000; 36; 16271638.
- [18] Van Der Merwe R. Sigma-point Kalman filters for probabilistic inference in dynamic state-space models. PhD, OGI School of Science & Engineering at Oregon Health & Science University, Portland, OR, USA, 2004.
- [19] Zhu J, Zheng N, Yuan Z, Zhang Q, Zhang X, He Y. A SLAM algorithm based on central difference Kalman filter. In: 2009 IEEE Intelligent Vehicle Symposium; 2009; Xian, China. New York, NY, USA: IEEE. pp. 123128.
- [20] Ankı¸shan H, Efe M. E¸szamanlı konum belirleme ve harita olu¸sturmaya Kalman filter yakla¸sımları. Dicle Universitesi ¨ M¨uhendislik Fak¨ultesi Dergisi 2010; 1: 1320.
- [21] Ji C, Zhang Y, Tong M, Yang S. Particle filter with swarm move for optimization. Lect Notes Comp Sci 2008; 5199: 909918.
- [22] Yanan W, Jie C, Minggang G. Improved differential evolution-based particle filter algorithm for target tracking. In: 30th Chinese Control Conference; 2011; Yantai, China. New York, NY, USA: IEEE. pp. 30093014.
- [23] Kwok NM, Fang G, Zhou W. Evolutionary particle filter: re-sampling from the genetic algorithm perspective. In: 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems; 2005. New York, NY, USA: IEEE.pp. 29352940.
- [24] Storn R, Price K. Differential Evolution A Simple and Efficient Adaptive Scheme for Global Optimization over Continuous Spaces. Technical Report TR-95-012. Berkeley, CA, USA: International Computer Science Institute, 1995.
- [25] Das S, Suganthan PN. Differential evolution: a survey of the state-of-the-art. IEEE T Evolut Comput 2011; 15: 431.
- [26] Vesterstrom J, Thomsen R. A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Congress on Evolutionary Computation; 2004. New York, NY, USA: IEEE. pp. 19801987.
- [27] Guivant J, Nedot E. Simultaneous Localization and Map Building: Test Case for Outdoor Applications. Sydney,Australia: Australian Centre for Field Robotics, Department of Mechanical and Mechatronic Engineering, The University of Sydney, Australia, 2006.