INITIAL POPULATION SIZE ESTIMATION FOR A DIFFERENTIAL-EVOLUTION-BASED GLOBAL LOCALIZATION FILTER

Fernando Martín, Luis Moreno, María L. Muñoz, and Dolores Blanco

References

  1. [1] L. Moreno, S. Garrido, and M.L. Muñoz, Evolutionary filter for robust mobile robot localization, Robotics and Autonomous Systems, 54(7), 2006, 590–600.
  2. [2] F. Martín, L. Moreno, S. Garrido, and D. Blanco, High-accuracy global localization filter for three-dimensional environments, Robotica, 30, 2012, 363–378.
  3. [3] R. Storn and K. Price, Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces, Journal of Global Optimization, 11, December 1997, 341–359.
  4. [4] T. Jansen, K.A.D. Jong, and I. Wegener, On the choice of the offspring population size in evolutionary algorithms, Evolutionary Computation, 13(4), 2005, 413–440.
  5. [5] W. Burgard, D. Fox, D. Henning, and T. Schmidt, Estimating the absolute position of a mobile robot using position probability grids, Proc. Fourteenth National Conference on Artificial Intelligence (AAAI’96), 1996.
  6. [6] D. Fox, J. Hightower, L. Liao, D. Schulz, and G. Borriello, Bayesian filters for location estimation, Pervasive Computing, 2, 2003, 24–33.
  7. [7] S. Thrun, D. Fox, W. Burgard, and F. Dellaert, Robust Monte Carlo localization for mobile robots, Artificial Intelligence, 128, 2001, 99–141.
  8. [8] C. Gamallo, C.V. Regueiro, P. Quint´ıa, and M. Mucientes, Omnivision-based KLD-Monte Carlo localization, Robotics and Autonomous Systems, 58, 2010, 295–305.
  9. [9] L. Zhang, R. Zapata, and P. Lepinay, Self-adaptive Monte-Carlo localization for mobile robots using range sensors, Proc. lEEW/RSJ International Conference on Intelligent Robots and System (IROS’09), 2009.
  10. [10] A.R. Vahdat, N.N. Ashrafoddin, and S.S. Ghidary, Mobile robot global localization using differential evolution and particle swarm optimization, Proc. Congress on Evolutionary Computation (CEC’07), 2007.
  11. [11] M. Mirkhania, R. Forsatib, M. Shahric, and A. Moayedikiad, A novel efficient algorithm for mobile robot localization, Robotics and Autonomous Systems, 2013.
  12. [12] Z. Geem, J. Kim, and G. Loganathan, A new heuristic optimization algorithm: harmony search, Simulation, 76(2), 2001, 60–78.
  13. [13] K.O. Arras, J.A. Castellanos, and R. Siegwart, Feature-based multi-hypothesis localization and tracking for mobile robots using geometric constraints, Proc. IEEE International Conference on Robotics and Automation (ICRA’02), Washington, DC, 2002, 1371–1377.
  14. [14] T. He and S. Hirose, A global localization approach based on Line-segment Relation Matching technique, Robotics and Autonomous Systems, 60, 2012, 95–112.
  15. [15] N. Ho and R. Jarvis, Vision based global localisation using a 3D environmental model created by a laser range scanner, Proc. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS’08), Acropolis Convention Center, Nice, France, September 2008.
  16. [16] S. Se, D.G. Lowe, and J.J. Little, Vision-based global localization and mapping for mobile robots, IEEE Transactions on Robotics, 21, 2005, 3.
  17. [17] D.G. Lowe, Object recognition from local scale-invariant features, in Proc. Seventh International Conference on Computer Vision (ICCV’99), 1999.
  18. [18] H. Bay, A. Ess, T. Tuytelaars, and L.V. Gool, SURF: Speeded Up Robust Features, Computer Vision and Image Understanding (CVIU), 110(3), 2008, 346–359.
  19. [19] L. Marchetti, G. Grisetti, and L. Iocchi, A comparative analysis of particle filter based localization methods, Proc. RoboCup Symposium, 2006.
  20. [20] N.J. Gordon, D.J. Salmond, and A.F.M. Smith, Novel approach to nonlinear/non-Gaussian Bayesian state estimation, Proc. IEEF Radar and Signal Processing, 140, 1993, 107–113.
  21. [21] M.K. Pitt and N. Shephard, Filtering via simulation: Auxiliary particle filters, Journal of the American Statistical Association, 94, 1999, 590–599.
  22. [22] R. Kmmerle, R. Triebel, P. Pfaff, and W. Burgard, Monte Carlo localization in outdoor terrains using multi-level surface maps, Journal of Field Robotics, 42, 2008, 213–222.
  23. [23] D. Fox, Adapting the sample size in particle filters through kldsampling, International Journal of Robotics Research, 22, 2003, 985–1003.
  24. [24] D. Koller and R. Fratkina, Using learning for approximation in stochastic processes, Proc. International Conference on Machine Learning (ICML’98), 1998.
  25. [25] G. Grisetti, C. Stachniss, and W. Burgard, Improving grid-based SLAM with Rao-Blackwellized particle filters by adaptive proposals and selective resampling, Proc. IEEE International Conference on Robotics and Automation (ICRA’05), 2005.
  26. [26] F. Bourgault, A.A. Makarenko, S.B. Williams, B. Grocholsky, and H.F. Durrant-Whyte, Information based adaptive robotic exploration, Proc. lEEW/RSJ International Conference on lntelligent Robots and Systems (IROS’02), 2002.
  27. [27] B. Grocholsky, Information-theoretic control of multiple sensor platforms, Ph.D. Thesis, Australian Centre for Field Robotics, Department of Aerospace, Mechatronic and Mechanical Engineering, The University of Sydney, 2002.
  28. [28] R. Rocha, J. Dias, and A. Carvalho, Cooperative multi-robot systems: A study of vision-based 3-D mapping using information theory, Robotics and Autonomous Systems, 53, 2005, 282–311.
  29. [29] S. Thrun, W. Burgard, and D. Fox, Probabilistic robotics (intelligent robotics and autonomous agents). (Cambridge, Massachusetts: MIT Press, 2005).
  30. [30] D.E. Goldberg, Genetic algorithm in search, optimization and machine learning. (Reading, Massachusetts: Addison Wesley Publishing Company, 1989).
  31. [31] S. Markon, D.V. Arnold, T. Back, T. Beielstein, and H.-G. Beyer, Thresholding – A selection operator for noisy ES, Proc. Congress on Evolutionary Computation (CEC’01), 2001.
  32. [32] N.M. Kwok, D.K. Liu, and G. Dissanayake, Evolutionary computing based mobile robot localization, Artificial Intelligence, 19, 2006, 857–868.

Important Links:

Go Back