Hostname: page-component-76fb5796d-x4r87 Total loading time: 0 Render date: 2024-04-26T13:32:24.532Z Has data issue: false hasContentIssue false
  • English
  • Français

A new compensation technique based on analysis of resampling process in FastSLAM

Published online by Cambridge University Press:  01 March 2008

Nosan Kwak ,
Gon-Woo Kim  and
Beom-Hee Lee
Nosan Kwak*
Affiliation:
School of Electrical Engineering, Seoul National University, Seoul 151-744, Korea.
Gon-Woo Kim
Affiliation:
Applied Robot Technology, Korea Institute of Technology, Cheonan, Chungnam 330-825, Korea.
Beom-Hee Lee
Affiliation:
School of Electrical Engineering, Seoul National University, Seoul 151-744, Korea.
*
*Corresponding author. E-mail: robot97@snu.ac.kr
Get access Rights & Permissions [Opens in a new window]

Summary

The state-of-the-art FastSLAM algorithm has been shown to cause a particle depletion problem while performing simultaneous localization and mapping for mobile robots. As a result, it always produces over-confident estimates of uncertainty as time progresses. This particle depletion problem is mainly due to the resampling process in FastSLAM, which tends to eliminate particles with low weights. Therefore, the number of particles to conduct loop-closure decreases, which makes the performance of FastSLAM degenerate. The resampling process has not been thoroughly analyzed even though it is the main reason for the particle depletion problem. In this paper, standard resampling algorithms (systematic residual and partial resampling), a rank-based resampling adopting genetic algorithms are analyzed using computer simulations. Several performance measures such as the effective sample size, the number of distinct particles, estimation errors, and complexity are used for the thorough analysis of the resampling algorithms. Moreover, a new compensation technique is proposed instead of resampling to resolve the particle depletion problem in FastSLAM. In estimation errors, the compensation technique outperformed other resampling algorithms though its run-time was longer than those of others. The most appropriate time to instigate compensation to reduce the run-time was also analyzed with the diminishing number of particles.

Keywords

FastSLAMParticle filterResamplingParticle depletionCompensationMobile robot
Type
Article
Information
Robotica , Volume 26 , Issue 2 , March 2008 , pp. 205 - 217
Copyright
Copyright © Cambridge University Press 2007

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Ekvall, S., Kragic, D. and Jensfelt, P.. “Object detection and mapping for service robot tasks,” Robotica 25 (2), 175–187 (2007).CrossRefGoogle Scholar
2.Leonard, J. L., Carpenter, R. N. and H. J. S. Feder. “Stochastic mapping using forward look sonar,” Robotica 19 (5), 467–480 (2001).CrossRefGoogle Scholar
3.Montemerlo, M., FastSLAM: A Factored Solution to the Simultaneous Localization and Mapping Problem With Unknown Data Association, Ph.D. Thesis, (Carnegie Mellon University, Pittsburgh, PA, 2003).Google Scholar
4.Fazli, S. and Kleeman, L., “Simultaneous landmark classification, localization and map building for an advanced sonar ring,” Robotica 25 (2), 1–14 (2006).CrossRefGoogle Scholar
5.Salichs, M. A. and Moreno, L., “Navigation of mobile robots: open questions,” Robotica 18 (3), 227–234 (2000).CrossRefGoogle Scholar
6.Newman, P., Leonard, J., Tardos, J. D. and Neira, J., “Explore and Return: Experimental Validation of Real-Time Concurrent Mapping and Localization,” IEEE International Conference on Robotics and Automation 2 (2002) pp. 1802–1809.Google Scholar
7.Doucet, A., de Freitas, N., Murphy, K. and Russell, S., “Rao–Blackwellised Particle Filtering for Dynamic Bayesian Networks,” Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence (2000) pp. 176–183.Google Scholar
8.Murphy, K., “Bayesian map learning in dynamic environments,” Adv. Neural Inform. Process. Syst. 12, 1015–1021 (1999).Google Scholar
9.Montemerlo, M. and Thrun, S., “Simultaneous localization and mapping with unknown data association using fastslam,” Proceedings of the 2003 IEEE International Conference on Robotics and Automation 2 (2003) pp. 1985–1991.Google Scholar
10.Thrun, S., Burgard, W. and Fox, D., Probabilistic Robotics (MIT Press, Cambridge, MA, 2005).Google Scholar
11.Bailey, T., Nieto, J. and Nebot, E., “Consistency of the FastSlam Algorithm,” Proceedings of the IEEE International Conference on Robotics and Automation (2006) pp. 424–427.Google Scholar
12.Kong, A., Liu, J. S. and Wong, W. H., “Sequential imputations and bayesian missing data problems,” J. Amer. Statist. Assoc. 89 (425), 278–288 (1994).CrossRefGoogle Scholar
13.Montemerlo, M., Thrun, S., Koller, D. and Wegbreit, B., “FastSlam 2.0: An Improved Particle Filtering Algorithm for Simultaneous Localization and Mapping that Provably Converges,” Proceedings of the 17th International Joint Conference on Artificial Intelligence (2003). pp. 1151–1156.Google Scholar
14.Grisetti, G., Tipaldi, G. D., Stachniss, C., Burgard, W. and Nardi, D., “Fast and accurate slam with Rao–Blackwelized particle filters,” Robot. Autonom. Syst. 55, 30–38 (2007).CrossRefGoogle Scholar
15.Doucet, A. and Gordon, N. J., “Simulation-based optimal filter for manoeuvring target tracking,” SPIE Proc. 3809, 241–255 (1999).CrossRefGoogle Scholar
16.van der Merwe, R., de Freitas, N., Doucet, A. and Wan, E., “The unscented particle filter, Cambridge University Engineering Department, 2000,” Technical Report CUED/F-INFENG/TR 380, Cambridge University, Cambridge, England (2000).Google Scholar
17.Liu, J. S., Chen, R. and Logvinenko, T., “A Theoretical Framework for Sequential Importance Sampling and Resampling,” In: Sequential Monte Carlo Methods in Practice (2001) pp. 225–246.Google Scholar
18.Gordon, N. J., Salmond, D. J. and Smith, A. F. M., “Novel approach to nonlinear/non-Gaussian Bayesian state estimation,” Radar Signal Process, IEE Proc. F 140 (2), 107–113 (1993).CrossRefGoogle Scholar
19.Grisetti, G., Stachniss, C. and Burgard, W., “Improved techniques for grid mapping with rao-blackwellized particle filters,” IEEE Trans. Robot. 23 (1), 34–46 (2007).CrossRefGoogle Scholar
20.Higuchi, T., “Monte Carlo filter using the genetic algorithm operators,” J. Statist. Comput. Simulation 59 (1), 1–23 (1997).CrossRefGoogle Scholar
21.Bolić, M., Djuric, P. M. and Hong, S. J., “Resampling algorithms for particle filters: A computational complexity perspective,” Eurasip J. Appl. Signal Process 15, 2267–2277 (2004).Google Scholar
22.Bolić, M., Djuric, P. M. and Hong, S. J., “New Resampling Algorithms for Particle Filters,” Proceedings of the 2003 IEEE International Conference on Acoustics, Speech, and Signal Processing 2 (2003). pp. 589–592.Google Scholar
23.Baker, J. E., “Adaptive Selection Methods for Genetic Algorithms,” Proceedings of the 1st International Conference on Genetic Algorithms Table of Contents (1985) pp. 101–111.Google Scholar
24.Durrant-Whyte, H. and Bailey, T., “Simultaneous localization and mapping: Part I,” IEEE Robot. Autom. Mag. 13 (2)99–108 (2006).CrossRefGoogle Scholar
25.Crisan, D. and Doucet, A., “A survey of convergence results on particle filtering methods for practitioners,” IEEE Trans. Signal Process. 50 (3), 736–746 (2002).CrossRefGoogle Scholar
26.Whitley, D., “The Genitor Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best,” Proceedings of the 3rd International Conference on Genetic Algorithms 1 (1989) pp. 116–121.Google Scholar
27.Liu, J. S., “Metropolized independent sampling with comparisons to rejection sampling and importance sampling,” Statist. Comput. 6 (2), 113–119 (1996).CrossRefGoogle Scholar
28.Stachniss, C., Grisetti, G. and Burgard, W., “Recovering Particle Diversity in a Rao–Blackwellized Particle Filter for Slam after Actively Closing Loops,” Proceedings of the IEEE International Conference on Robotics and Automation (2005). pp. 655–660.Google Scholar
29.Bäck, T., Fogel, D. B. and Michalewicz, Z., Evolutionary Computation 1: Basic Algorithms and Operators (Institute of Physics Publishing, Bristol, U.K., 2000).CrossRefGoogle Scholar