Abstract
One of the most challenging aspects of concurrent mapping and localization (CML) is the problem of data association. Because of uncertainty in the origins of sensor measurements, it is difficult to determine the correspondence between measured data and features of the scene or object being observed, while rejecting spurious measurements. This paper reviews several new approaches to data association and feature modeling for CML that share the common theme of combining information from multiple uncertain vantage points while rejecting spurious data. Our results include: (1) feature-based mapping from laser data using robust segmentation, (2) map-building with sonar data using a novel application of the Hough transform for perception grouping, and (3) a new stochastic framework for making delayed decisions for combination of data from multiple uncertain vantage points. Experimental results are shown for CML using laser and sonar data from a B21 mobile robot.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Anderson BDO, Moore JB (1979) Optimal filtering. Prentice-Hall, Englewood Cliffs, N.J.
Ballard D, Brown C (1982) Computer vision. Prentice-Hall.
Bar-Shalom Y, Fortmann TE (1988) Tracking and data association. Academic Press.
Brooks RA (1984) Aspects of mobile robot visual map making. In Second Int. Symp. Robotics Research, pp 287–293, Tokyo, Japan. MIT Press.
Castellanos JA, Tardos JD (2000) Mobile robot localization and map building: A multisensor fusion approach. Kluwer Academic Publishers, Boston.
Chatila R (1985) Mobile robot navigation space modeling and decisional processes. In Third Int. Symposium Robotics Research. MIT Press.
Davison AJ (1998) Mobile robot navigation using active vision. PhD thesis, University of Oxford.
Dissanayake MWMG, Newman P, Durrant-Whyte HF, Clark S, Csorba M (2001) A solution to the simultaneous localization and map building (slam) problem. IEEE Transactions on Robotic and Automation, 17(3):229–241.
Feder HJS, Leonard JJ, Smith CM (1999) Adaptive mobile robot navigation and mapping. Int. J. Robotics Research, 18(7): 650–668.
Guivant J, Nebot E (2001) Optimization of the simultaneous localization and map building algorithm for real time implementation. IEEE Transactions on Robotic and Automation, 17(3):242–257.
Gutmann JS, Konolige K (1999) Incremental mapping of large cyclic environments. In International Symposium on Computational Intelligence in Robotics and Automation.
Leonard JJ, Durrant-Whyte HF (1992) Directed sonar sensing for mobile robot navigation. Kluwer Academic Publishers, Boston.
Leonard JJ, Feder HJS (2000) A computationally efficient method for large-scale concurrent mapping and localization. In Koditschek D, Hollerbach J (eds) Robotics Research: The Ninth International Symposium, pp 169–176, Snowbird, Utah. Springer Verlag.
Leonard JJ, Rikoski R (2001) Incorporation of delayed decision making into stochastic mapping. In Rus D, Singh S (eds) Experimental Robotics VII, Lecture Notes in Control and Information Sciences. Springer-Verlag.
Leonard JJ, Rikoski RJ, Newman PM, Bosse M (2002) Mapping partially observable features from multiple uncertain vantage points. Int. J. Robotics Research. To Appear.
Moutarlier P, Chatila R (1989) Stochastic multi-sensory data fusion for mobile robot location and environment modeling. In 5th Int. Symposium on Robotics Research, pp 207–216.
Neira J, Tardós J (2001) Data association in stochastic mapping using the joint compatibility test. IEEE Trans. on Robotics and Automation, 17(6):890–897.
Smith R, Cheeseman P (1987) On the representation and estimation of spatial uncertainty. International Journal of Robotics Research, 5(4):56.
Smith R, Self M, Cheeseman P (1990) Estimating uncertain spatial relationships in robotics. In Cox I, Wilfong G (eds) Autonomous Robot Vehicles, pp 167–193. Springer-Verlag.
Tardós J, Neira J, Newman PM, Leonard JJ (2002) Robust mapping and localization in indoor environments using sonar data. Int. J. Robotics Research. To Appear.
Thrun S (2001) An online mapping algorithm for teams of mobile robots. Int. J. Robotics Research, 20(5):335–363.
Wijk O, Christensen H (2000) Triangulation based fusion of sonar data with application in robot pose tracking. IEEE Trans. Robotics and Automation, 16(6):740–752.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Leonard, J.J., Newman, P.M., Rikoski, R.J., Neira, J., Tardós, J.D. (2003). Towards Robust Data Association and Feature Modeling for Concurrent Mapping and Localization. In: Jarvis, R.A., Zelinsky, A. (eds) Robotics Research. Springer Tracts in Advanced Robotics, vol 6. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36460-9_1
Download citation
DOI: https://doi.org/10.1007/3-540-36460-9_1
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-00550-6
Online ISBN: 978-3-540-36460-3
eBook Packages: Springer Book Archive