Autonomous Robots

, Volume 24, Issue 3, pp 315–335 | Cite as

A practical approach for EKF-SLAM in an indoor environment: fusing ultrasonic sensors and stereo camera

  • SungHwan Ahn
  • Jinwoo Choi
  • Nakju Lett Doh
  • Wan Kyun Chung
Article

Abstract

Improving the practical capability of SLAM requires effective sensor fusion to cope with the large uncertainties from the sensors and environment. Fusing ultrasonic and vision sensors possesses advantages of both economical efficiency and complementary cooperation. In particular, it can resolve the false data association and divergence problem of an ultrasonic sensor-only algorithm and overcome both the low frequency of SLAM update caused by the computational burden and the weakness to illumination changes of a vision sensor-only algorithm. In this paper, we propose a VR-SLAM (Vision and Range sensor-SLAM) algorithm to combine ultrasonic sensors and stereo camera very effectively. It consists of two schemes: (1) extracting robust point and line features from sonar data and (2) recognizing planar visual objects using a multi-scale Harris corner detector and its SIFT descriptor from a pre-constructed object database. We show that fusing these schemes through EKF-SLAM frameworks can achieve correct data association via the object recognition and high frequency update via the sonar features. The performance of the proposed algorithm was verified by experiments in various real indoor environments.

Keywords

Ultrasonic sensor Sonar feature detection Stereo camera Visual object recognition SLAM Mobile robot 

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References

  1. Ahn, S., Choi, M., Choi, J., & Chung, W. K. (2006). Data association using visual object recognition for EKF-SLAM in home environment. In Proc. of IEEE/RSJ international conference on intelligent robots and systems (pp. 2588–2594). Google Scholar
  2. Barfoot, T. D. (2005). Online visual motion estimation using FastSLAM with SIFT features. In Proc. of IEEE/RSJ international conference on intelligent robots and systems (pp. 579–585). Google Scholar
  3. Bosse, M., Newman, P., Leonard, J., & Teller, S. (2004). SLAM in large-scale cyclic environments using the ATLAS framework. International Journal on Robotics and Research, 23(12), 1113–1139. CrossRefGoogle Scholar
  4. Choi, J., Ahn, S., & Chung, W. K. (2005). Robust sonar feature detection for the SLAM of mobile robot. In Proc. of IEEE/RSJ international conference on intelligent robots and systems (pp. 3415–3420). Google Scholar
  5. Choset, H., Nagatani, K., & Lazar, N. A. (2003). The arc-transversal median algorithm: A geometric approach to increasing ultrasonic sensor azimuth accuracy. IEEE Transactions on Robotics and Automation, 19(3), 513–523. CrossRefGoogle Scholar
  6. Davison, A. J. (2003). Real-time simultaneous localisation and mapping with a single camera. In Proc. of international conference on computer vision (pp. 1403–1410). Google Scholar
  7. Dissanayake, M.W.M.G., Newman, P., Clark, S., Durrant-Whyte, H.F., & Csorba, M. (2001). A solution to the simultaneous localization and map building (SLAM) problem. IEEE Transactions on Robotics and Automation, 17(3), 229–241. CrossRefGoogle Scholar
  8. Elinas, P., Sim, R., & Little, J. J. (2006). σSLAM: Stereo vision SLAM using the Rao-Blackwellised particle filter and a novel mixture proposal distribution. In Proc. of IEEE international conference on robotics and automation (pp. 1564–1570). Google Scholar
  9. Estrada, C., Neira, J., & Tardós, J.D. (2005). Hierarchical SLAM: Real-time accurate mapping of large environment. IEEE Transactions on Robotics, 21(4), 588–596. CrossRefGoogle Scholar
  10. Fischler, M. A., & Bolles, R. C. (1981). Random sample consensus: A paradigm for model fitting with application to image analysis and automated cartography. Communications of the ACM, 24(6), 381–395. CrossRefMathSciNetGoogle Scholar
  11. Folkesson, J., Jensfelt, P., & Christensen, H. I. (2005). Graphical SLAM using vision and the measurement subspace. In Proc. of IEEE/RSJ international conference on intelligent robots and systems (pp. 325–330). Google Scholar
  12. Guivant, J. E., & Nebot, E. M. (2001). Optimization of the simultaneous localization and map-building algorithms for real-time implementation. IEEE Transactions on Robotics and Automation, 17(3), 242–257. CrossRefGoogle Scholar
  13. Jeong, W., & Lee, K. M. (2005). CV-SLAM: A new ceiling vision-based SLAM technique. In Proc. of IEEE/RSJ international conference on intelligent robots and systems (pp. 3195–3200). Google Scholar
  14. Karlsson, N., Bernardo, E. D., Ostrowski, J., Goncalves, L., Pirjanian, P., & Munich, M. E. (2005). The vSLAM algorithm for robust localization and mapping. In Proc. of IEEE international conference on robotics and automation (pp. 24–29). Google Scholar
  15. Leonard, J. J., & Durrant-Whyte, H. F. (1991). Mobile robot localization by tracking geometric beacons. IEEE Transactions on Robotics and Automation, 7(3), 376–382. CrossRefGoogle Scholar
  16. Lin, Z., Kim, S., & Kweon, I. S. (2005). Recognition-based indoor topological navigation using robust invariant features. In Proc. of IEEE/RSJ international conference on intelligent robots and systems (pp. 2309–2314). Google Scholar
  17. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110. CrossRefGoogle Scholar
  18. Mikolajczyk, K., & Schmid, C. (2004). Scale and affine invariant interest point detectors. International Journal of Computer Vision, 60(1), 63–86. CrossRefGoogle Scholar
  19. Mikolajczyk, K., & Schmid, C. (2005). A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(10), 1615–1630. CrossRefGoogle Scholar
  20. Montemerlo, M., Thrun, S., Koller, D., & Wegbreit, B. (2003). FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges. In Proc. of the sixteenth international joint conference on artificial intelligence (pp. 1151–1156). Google Scholar
  21. Newman, P., & Ho, K. (2005). SLAM-Loop closing with visually salient features. In Proc. of IEEE international conference on robotics and automation (pp. 635–642). Google Scholar
  22. Newman, P., Cole, D., & Ho, K. (2006). Outdoor SLAM using visual appearance and laser ranging. In Proc. of IEEE international conference on robotics and automation (pp. 1180–1187). Google Scholar
  23. Ortin, D., Neira, J., & Montiel, J. M. M. (2003). Relocation using laser and vision. In Proc. of IEEE international conference on robotics and automation (pp. 1505–1510). Google Scholar
  24. Se, S., Lowe, D. G., & Little, J. (2002). Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks. International Journal of Robotics Research, 21(8), 735–758. CrossRefGoogle Scholar
  25. Tardós, J. D., Neira, J., Newman, P. M., & Leonard, J. J. (2002). Robust mapping and localization in indoor environments using sonar data. International Journal of Robotics Research, 21(4), 311–330. CrossRefGoogle Scholar
  26. Wijk, O., & Christensen, H. I. (2000). Triangulation-based fusion of sonar data with application in robot tracking. IEEE Transactions on Robotics and Automation, 16(6), 740–752. CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • SungHwan Ahn
    • 1
  • Jinwoo Choi
    • 1
  • Nakju Lett Doh
    • 2
  • Wan Kyun Chung
    • 1
  1. 1.Robotics Lab., Department of Mechanical EngineeringPohang University of Science & Technology (POSTECH)PohangKorea
  2. 2.School of Electrical EngineeringKorea UniversitySeoulKorea

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