Abstract
In this paper, we propose a robust pose tracking method for mobile robot localization with an incomplete map in a highly non-static environment. This algorithm will work with a simple map that does not include complete information about the non-static environment. With only an initial incomplete map, a mobile robot cannot estimate its pose because of the inconsistency between the real observations from the environment and the predicted observations on the incomplete map. The proposed localization algorithm uses the approach of sampling from a non-corrupted window, which allows the mobile robot to estimate its pose more robustly in a non-static environment even when subjected to severe corruption of observations. The algorithm sequence involves identifying the corruption by comparing the real observations with the corresponding predicted observations of all particles, sampling particles from a non-corrupted window that consists of multiple non-corrupted sets, and filtering sensor measurements to provide weights to particles in the corrupted sets. After localization, the estimated path may still contain some errors due to long-term corruption. These errors can be corrected using nonlinear constrained least-squares optimization. The incomplete map is then updated using both the corrected path and the stored sensor information. The performance of the proposed algorithm was verified via simulations and experiments in various highly non-static environments. Our localization algorithm can increase the success rate of tracking its pose to more than 95% compared to estimates made without its use. After that, the initial incomplete map is updated based on the localization result.
Similar content being viewed by others
References
Arulampalam, M. S., Maskell, S., Gordon, N., & Clapp, T. (2002). A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on Signal Processing, 50(2), 174–188.
Bergman, N. (1999). Recursive Bayesian estimation: navigation and tracking applications. Ph.D. Dissertation, Linköping University, Linköping, Sweden.
Biber, P., & Duckett, T. (2009). Experimental analysis of sample-based maps for long-term SLAM. The International Journal of Robotics Research, 28(1), 20–33.
Chung, W. K., Ahn, S., Lee, J., Lee, K., Choi, J., & Choi, M. (2007). POSTECH navigation frame: toward a practical solution for indoor SLAM and navigation. In Proc. of 13th int. symposium of robotics research (pp. 277–288).
Dellaert, F., Burgard, W., Fox, D., & Thrun, S. (1999a). Using the CONDENSATION algorithm for robust, vision-based mobile robot localization. In Proc. of IEEE computer society conf. on computer vision and pattern recognition (pp. 588–594).
Dellaert, F., Fox, D., Burgard, W., & Thrun, S. (1999b). Monte Carlo localization for mobile robots. In Proc. of IEEE int. conf. on robotics and automation (pp. 1322–1328).
Doucet, A., Freitas, N. de, & Gordon, N. (2001). Sequential Monte Carlo in practice. New York: Springer.
Doucet, A., Godsill, S., & Andrieu, C. (2000). On sequential Monte Carlo sampling methods for Bayesian filtering. Statistics and Computing, 10(3), 197–208.
Estrada, C., Neira, J., & Tardós, J. D. (2005). Hierarchical SLAM: real-time accurate mapping of large environments. IEEE Transactions on Robotics, 21(4), 588–596.
Fox, D. (2001). KLD-sampling: adaptive particle filters. In Advances in neural information processing systems 14.
Fox, D., Burgard, W., Dellaert, F., & Thrun, S. (1999). Monte Carlo localization: efficient position estimation for mobile robots. In Proc. of the 16th national conf. on artificial intelligence.
Gordon, N. J., Salmond, D. J., & Smith, A. F. M. (1993). Novel approach to nonlinear/non-Gaussian Bayesian state estimation. IEE Proceedings F Radar and Signal Processing, 140(2), 107–113.
Jensfelt, P., Austin, D. J., Wijk, O., & Andersson, M. (2000a). Feature based condensation for mobile robot localization. In Proc. of IEEE int. conf. on robotics and automation (pp. 2531–2537).
Jensfelt, P., Wijk, O., Austin, D. J., & Andersson, M. (2000b). Experiments on augmenting CONDENSATION for mobile robot localization. In Proc. of IEEE int. conf. on robotics and automation (pp. 2518–2524).
Kanazawa, K., Koller, D., & Russell, S. (1995). Stochastic simulation algorithms for dynamic probabilistic networks. In Proc. of the 11th conf. on uncertainty in artificial intelligence (pp. 346–351).
Kwok, C., Fox, D., & Meila, M. (2002). Real-time particle filters. In Advances in neural information processing systems 15.
Kwok, C., Fox, D., & Meila, M. (2003). Adaptive real-time particle filters for robot localization. In Proc. of IEEE int. conf. on robotics and automation (pp. 2836–2841).
Lee, D., & Chung, W. (2005). Dependable localization strategy in dynamic real environments. In Proc. of IEEE/RSJ int. conf. on intelligent robots and systems (pp. 3746–3751).
Lee, J., & Chung, W. K. (2008). Robust particle filter localization by sampling from non-corrupted window with incomplete map. In Proc. of IEEE/RSJ int. conf. on intelligent robots and systems (pp. 1133–1139).
Lee, K., Suh, I. H., Oh, S., & Chung, W. K. (2008). Conflict evaluation method for grid maps using sonar sensors. In Proc. of IEEE/RSJ int. conf. on intelligent robots and systems (pp. 2908–2914).
Lenser, S., & Veloso, M. (2000). Sensor resetting localization for poorly modelled mobile robots. In Proc. of IEEE int. conf. on robotics and automation (pp. 1225–1232).
MacCormick, J., & Blake, A. (1999). A probabilistic exclusion principle for tracking multiple objects. In Proc. of the 7th IEEE int. conf. on computer vision (pp. 572–578).
Moravec, H. P. (1988). Sensor fusion in certainty grids for mobile robots. AI Magazine, 9(2), 61–74.
Oriolo, G., Ulivi, G., & Vendittelli, M. (1997). Fuzzy maps: a new tool for mobile robot perception and planning. Journal of Robotic Systems, 14, 179–197.
Pagac, D., Nebot, E. M., & Durrant-Whyte, H. (1998). An evidential approach to map-building for autonomous vehicles. IEEE Transactions on Robotics and Automation, 14(4), 623–629.
Siciliano, B., & Khatib, O. (2008). Handbook of robotics. Berlin/Heidelberg: Springer.
Smith, R. C., & Cheeseman, P. (1986). On the representation and estimation of spatial uncertainty. The International Journal of Robotics Research, 5(4), 56–68.
Smith, R., Self, M., & Cheeseman, P. (1990). Estimating uncertain spatial relationships in robotics. In I. J. Cox, & G. T. Wilfong (Eds.), Autonomous robot vehicles (pp. 167–193). New York: Springer.
Stachniss, C., & Burgard, W. (2005). Mobile robot mapping and localization in non-static environments. In Proc. of the national conference on artificial intelligence (AAAI) (pp. 1324–1329).
Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. New York: MIT Press.
Thrun, S., Fox, D., Burgard, W., & Dellaert, F. (2001). Robust Monte Carlo localization for mobile robots. Artificial Intelligence, 128(1–2), 99–141.
Wolf, D. F., & Sukhatme, G. S. (2005). Mobile robot simultaneous localization and mapping in dynamic environments. Autonomous Robots, 19(1), 53–65.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Lee, JS., Chung, W.K. Robust mobile robot localization in highly non-static environments. Auton Robot 29, 1–16 (2010). https://doi.org/10.1007/s10514-010-9184-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10514-010-9184-1