Abstract
In robot localization problems, uncertainty arises from many factors and must be considered together with the model constraints. Probabilistic robotics is the classical approach for dealing with hard robotic problems that relies on probability theory. This work describes the application of probabilistic constraint techniques in the context of probabilistic robotics to solve robot localization problems. Instead of providing the most probable position of the robot, the approach characterizes all positions consistent with the model and their probabilities (in accordance with the underlying uncertainty). It relies on constraint programming to get a tight covering of the consistent regions combined with Monte Carlo integration techniques that benefit from such reduction of the sampling space.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. The MIT Press (2006)
Särkkä, S.: Bayesian filtering and smoothing. Cambridge University Press (2013)
Kalman, R.E.: A new approach to linear filtering and prediction problems. ASME Journal of Basic Engineering (1960)
Julier, S.J., Jeffrey, Uhlmann, K.: Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 401–422 (2004)
Kaplow, R., Atrash, A., Pineau, J.: Variable resolution decomposition for robotic navigation under a pomdp framework. In: IEEE Robotics and Automation, pp. 369–376 (2010)
Arulampalam, M., Maskell, S., Gordon, N.: A tutorial on particle filters for online nonlinear/non-gaussian bayesian tracking. IEEE Trans. Signal Proc. 50, 174–188 (2002)
Wang, Y., Wu, D., Seifzadeh, S., Chen, J.: A moving grid cell based mcl algorithm for mobile robot localization. In: IEEE Robotics and Biomimetics, pp. 2445–2450 (2009)
Dellaert, F., Fox, D., Burgard, W., Thrun, S.: Monte carlo localization for mobile robots. In: IEEE Robotics and Automation, pp. 1322–1328 (1999)
Lhomme, O.: Consistency techniques for numeric CSPs. In: Proc. of the 13th IJCAI (1993)
Benhamou, F., McAllester, D., van Hentenryck, P.: CLP(intervals) revisited. In: ISLP, pp. 124–138. MIT Press (1994)
Hentenryck, P.V., Mcallester, D., Kapur, D.: Solving polynomial systems using a branch and prune approach. SIAM Journal Numerical Analysis 34, 797–827 (1997)
Benhamou, F., Goualard, F., Granvilliers, L., Puget, J.F.: Revising hull and box consistency. In: Procs. of ICLP, pp. 230–244. MIT (1999)
Moore, R.: Interval Analysis. Prentice-Hall, Englewood Cliffs (1966)
Carvalho, E.: Probabilistic Constraint Reasoning. PhD thesis, FCT/UNL (2012)
Hammersley, J., Handscomb, D.: Monte Carlo Methods. Methuen, London (1964)
Granvilliers, L., Benhamou, F.: Algorithm 852: Realpaver an interval solver using constraint satisfaction techniques. ACM Trans. Mathematical Software 32(1), 138–156 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Correia, M., Meshcheryakova, O., Sousa, P., Cruz, J. (2015). Probabilistic Constraints for Robot Localization. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_47
Download citation
DOI: https://doi.org/10.1007/978-3-319-23485-4_47
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-23484-7
Online ISBN: 978-3-319-23485-4
eBook Packages: Computer ScienceComputer Science (R0)