Skip to main content
Log in

Robust vision-based robot localization using combinations of local feature region detectors

Autonomous Robots Aims and scope Submit manuscript

Abstract

This paper presents a vision-based approach for mobile robot localization. The model of the environment is topological. The new approach characterizes a place using a signature. This signature consists of a constellation of descriptors computed over different types of local affine covariant regions extracted from an omnidirectional image acquired rotating a standard camera with a pan-tilt unit. This type of representation permits a reliable and distinctive environment modelling. Our objectives were to validate the proposed method in indoor environments and, also, to find out if the combination of complementary local feature region detectors improves the localization versus using a single region detector. Our experimental results show that if false matches are effectively rejected, the combination of different covariant affine region detectors increases notably the performance of the approach by combining the different strengths of the individual detectors. In order to reduce the localization time, two strategies are evaluated: re-ranking the map nodes using a global similarity measure and using standard perspective view field of 45°.

In order to systematically test topological localization methods, another contribution proposed in this work is a novel method to see the degradation in localization performance as the robot moves away from the point where the original signature was acquired. This allows to know the robustness of the proposed signature. In order for this to be effective, it must be done in several, variated, environments that test all the possible situations in which the robot may have to perform localization.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  • Andreasson, H., Treptow, A., & Duckett, T. (2005). Localization for mobile robots using panoramic vision, local features and particle filters. In: Proceedings of the IEEE international conference on robotics and automation (ICRA’05), Barcelona, Spain.

  • Angeli, A., Filliat, D., Doncieux, S., & Meyer, A. J. (2008). A fast and incremental method for loop-closure detection using bags of visual words. IEEE Transactions on Robotics, Special Issue on Visual SLAM.

  • Bay, H., Ess, A., Tuytelaars, T., & Gool, L. V. (2008). Surf: Speeded up robust features. Computer Vision and Image Understanding, 110(3), 346–359.

    Article  Google Scholar 

  • Beeson, P., Jong, K. N., & Kuipers, B. (2005). Towards autonomous topological place detection using the extended Voronoi graph. In: IEEE international conference on robotics and automaton (ICRA), Barcelona, Spain, pp. 4373–4379.

  • Booij, O., Terwijn, B., Zivkovic, Z., & Krose, B. (2007). Navigation using an appearance based topological map. In: IEEE international conference on robotics and automation (ICRA), pp. 3927–3932.

  • Brown, M., & Lowe, D. (2003). Recognising panoramas. In ICCV ’03: Proceedings of the ninth IEEE international conference on computer vision (p. 1218). Washington: IEEE Computer Society.

    Chapter  Google Scholar 

  • Canny, J. (1986). A computational approach to edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6), 679–698.

    Article  Google Scholar 

  • Castellanos, A. J., & Tardos, D. J. (1999). Mobile robot localization and map building: Multisensor fusion approach. Dordrecht: Kluwer.

    Google Scholar 

  • Choset, H., & Nagatani, K. (2001). Topological simultaneous localization and mapping (SLAM): Toward exact localization without explicit localization. IEEE Transactions on Robotics and Automation, 17(2), 125–137.

    Article  Google Scholar 

  • Cummins, M., & Newman, P. (2008). Accelerated appearance-only SLAM. In: Robotics and automation, 2008. ICRA 2008. IEEE international conference on, pp. 1828–1833.

  • Dissanayake, M., Newman, P., Clark, S. M., Durrant-Whyte, H., & Csorba, M. (2001). A solution to the simultaneous localization and map building (slam) problem. IEEE Transactions on Robotics and Automation, 17(3), 229–241.

    Article  Google Scholar 

  • Franz, M., Schlkopf, O., Mallot, B., & Blthoff, A. H. (1998). Learning view graphs for robot navigation. Autonomous Robots, 5, 111–125.

    Article  Google Scholar 

  • Hartley, R., & Zisserman, A. (2004). Multiple view geometry in computer vision, 2nd edn. Cambridge: Cambridge University Press, ISBN: 0521540518.

    MATH  Google Scholar 

  • Lindeberg, T. (1998). Feature detection with automatic scale selection. International Journal of Computer Vision, 30(2), 79–116.

    Article  Google Scholar 

  • Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.

    Article  Google Scholar 

  • Matas, J., Chum, O., Urban, M., & Pajdla, T. (2002). Robust wide baseline stereo from maximally stable extremal regions. In: Proceedings of the British machine vision conference (BMVC’02), Cardiff, UK.

  • Mikolajczyk, K., & Schmid, C. (2004). Scale & affine invariant interest point detectors. International Journal of Computer Vision, 60(1), 63–86.

    Article  Google Scholar 

  • Mikolajczyk, K., & Schmid, C. (2005). A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis & Machine Intelligence, 27(10), 1615–1630.

    Article  Google Scholar 

  • Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., & van Gool, L. (2005). A comparison of affine region detectors. International Journal of Computer Vision, 65(2), 43–72.

    Article  Google Scholar 

  • Nister, D., & Stewenius, H. (2006). Scalable recognition with a vocabulary tree. Conf. Computer Vision and Pattern Recognition, 2, 2161–2168.

    Google Scholar 

  • Owen, C., & Nehmzow, U. (1998). Landmark-based navigation for a mobile robot. In From animals to animals: Fifth international conference on simulation of adaptive behavior (SAB) (pp. 240–245). Cambridge: MIT.

    Google Scholar 

  • Ramisa, A., Tapus, A., Lopez de Mantaras, R., & Toledo, R. (2008). Mobile robot localization using panoramic vision and combinations of feature region detectors. In: Robotics and automation, 2008. ICRA 2008. IEEE international conference on, pp. 538–543.

  • Se, S., Lowe, D., & Little, J. (2002). Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. International Journal of Robotics Research, 21(8), 735–758.

    Article  Google Scholar 

  • Shum, H., & Szeliski, R. (1997). Panoramic image mosaics. Tech. Rep. MSR-TR-97-23, Microsoft Research.

  • Silpa-Anan, C., & Hartley, R. A. (2004). Localization using an image-map. In: Proceedings of the 2004 Australasian conference on robotics and automation, Canberra, Australia.

  • Szeliski, R., & Shum, H. Y. (1997). Creating full view panoramic image mosaics and environment maps. In SIGGRAPH ’97: Proceedings of the 24th annual conference on computer graphics and interactive techniques, vol. 31 (pp. 251–258). New York: ACM/Addison-Wesley.

    Chapter  Google Scholar 

  • Tapus, A., & Siegwart, R. (2006). A cognitive modeling of space using fingerprints of places for mobile robot navigation. In: Proceedings IEEE international conference on robotics and automation (ICRA), Orlando, USA, pp. 1188–1193.

  • Thrun, S. (1998). Learning metric-topological maps for indoor mobile robot navigation. Artificial Intelligence, 99(1), 21–71.

    Article  MATH  Google Scholar 

  • Thrun, S. (2000). Probabilistic algorithms in robotics. Artificial Intelligence Magazine, 21, 93–109.

    Google Scholar 

  • Uyttendaele, M., Eden, A., & Szeliski, R. (2001). Eliminating ghosting and exposure artifacts in image mosaics. In: IEEE computer society conference on computer vision and pattern recognition. IEEE Computer Society, vol. 2.

  • Valgren, C., & Lilienthal, A. (2008). Incremental spectral clustering and seasons: Appearance-based localization in outdoor environments. In: Robotics and automation, 2008. ICRA 2008. IEEE international conference on, pp. 1856–1861.

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Arnau Ramisa.

Additional information

This work was partially supported by the FI grant from the Generalitat de Catalunya, the European Social Fund, and the grant 2009-SGR-1434 and the MID-CBR project grant TIN2006-15140-C03-01 and FEDER funds and the grant 2005-SGR-00093 and the MIPRCV Consolider Ingenio 2010.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ramisa, A., Tapus, A., Aldavert, D. et al. Robust vision-based robot localization using combinations of local feature region detectors. Auton Robot 27, 373–385 (2009). https://doi.org/10.1007/s10514-009-9136-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10514-009-9136-9

Keywords

Navigation