SIMULTANEOUS LOCALIZATION AND MAPPING IN UNMODIFIED ENVIRONMENTS USING STEREO VISION

A. Gil, O. Reinoso, C. Fernández, M. A. Vicente, A. Rottmann, O. Martínez Mozos

Abstract

In this paper we describe an approach that builds three dimensional maps using visual landmarks extracted from images of an unmodified environment. We propose a solution to the Simultaneous Localization and Mapping (SLAM) problem for autonomous mobile robots using visual landmarks. Our map is represented by a set of three dimensional landmarks referred to a global reference frame, each landmark contains a visual descriptor that partially differentiates it from others. Significant points extracted from stereo images are used as natural landmarks, in particular we employ SIFT features found in the environment. We estimate both the map and the path of the robot using a Rao-Blackwellized particle filter, thus the problem is decomposed into two parts: one estimation over robot paths using a particle filter, and N independent estimations over landmark positions, each one conditioned on the path estimate. We actively track visual landmarks at a local neighbourhood and select only those that are more stable. When a visual feature has been observed from a significant number of frames it is then integrated in the filter. By this procedure, the total number of landmarks in the map is reduced, compared to prior approaches. Due to the tracking of each landmark, we obtain different examples that represent the same natural landmark. We use this fact to improve data association. Finally, efficient resampling techniques have been applied, which reduces the number of particles needed and avoids the particle depletion problem.

References

  1. Dissanayake, G., Newman, P., Clark, S., Durrant-Whyte, H., and Csorba, M. (2001). A solution to the simultaneous localization and map building (slam) problem. IEEE Trans. on Robotics and Automation, 17:229- 241.
  2. Gil, A., Reinoso, O., Vicente, A., Fernández, C., and Payá, L. (2005). Monte carlo localization using sift features. Lecture Notes in Computer Science (LNCS), 1(3523):623-630.
  3. Little, J., Se, S., and Lowe, D. (2001). Vision-based mobile robot localization and mapping using scaleinvariant features. In Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pages 2051-2058.
  4. Little, J., Se, S., and Lowe, D. (2002). Global localization using distinctive visual features. In Proceedings of the 2002 IEEE/RSJ Intl. Conference on Intelligent Robots and Systems.
  5. Lowe, D. (1999). Object recognition from local scaleinvariant features. In International Conference on Computer Vision, pages 1150-1157.
  6. Lowe, D. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 2(60):91-110.
  7. Miró, J. V., Dissanayake, G., and Zhou, W. (2005). Visionbased slam using natural features in indoor environments. In Proceedings of the 2005 IEEE International Conference on Intelligent Networks, Sensor Networks and Information Processing, pages 151-156.
  8. Montemerlo, M., Thrun, S., Koller, D., and Wegbreit, B. (2002). Fastslam: A factored solution to the simultaneous localization and mapping problem. In AAAI.
  9. Murphy, K. (1999). Bayesian map learning in dynamic environments. In In Neural Information Processing Systems (NIPS).
  10. Sim, R., Elinas, P., Griffin, M., and Little, J. J. (2005). Vision-based slam using the rao-blackwellised particle filter. In IJCAI Workshop on Reasoning with Uncertainty in Robotics.
  11. Stachniss, C., Haehnel, D., and Burgard, W. (2004). Exploration with active loop-closing for FastSLAM. In IEEE/RSJ Int. Conference on Intelligent Robots and Systems.
  12. Stachniss, C., Haehnel, D., and Burgard, W. (2005). Improving grid-based slam with rao-blackwellized particle filters by adaptive proposals and selective resampling. In IEEE Int. Conference on Robotics and Automation (ICRA).
  13. Thrun, S. (2001). A probabilistic online mapping algorithm for teams of mobile robots. International Journal of Robotics Research, 20(5):335-363.
  14. Wijk, O. and Christensen, H. I. (2000). Localization and navigation of a mobile robot using natural point landmarkd extracted from sonar data. Robotics and Autonomous Systems, 1(31):31-42.
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Paper Citation


in Harvard Style

Gil A., Reinoso O., Fernández C., A. Vicente M., Rottmann A. and Martínez Mozos O. (2006). SIMULTANEOUS LOCALIZATION AND MAPPING IN UNMODIFIED ENVIRONMENTS USING STEREO VISION . In Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO, ISBN 978-972-8865-60-3, pages 302-309. DOI: 10.5220/0001207303020309


in Bibtex Style

@conference{icinco06,
author={A. Gil and O. Reinoso and C. Fernández and M. A. Vicente and A. Rottmann and O. Martínez Mozos},
title={SIMULTANEOUS LOCALIZATION AND MAPPING IN UNMODIFIED ENVIRONMENTS USING STEREO VISION},
booktitle={Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,},
year={2006},
pages={302-309},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001207303020309},
isbn={978-972-8865-60-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Third International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO,
TI - SIMULTANEOUS LOCALIZATION AND MAPPING IN UNMODIFIED ENVIRONMENTS USING STEREO VISION
SN - 978-972-8865-60-3
AU - Gil A.
AU - Reinoso O.
AU - Fernández C.
AU - A. Vicente M.
AU - Rottmann A.
AU - Martínez Mozos O.
PY - 2006
SP - 302
EP - 309
DO - 10.5220/0001207303020309