Autonomous Robots

, Volume 30, Issue 4, pp 351–368 | Cite as

Autonomous topological modeling of a home environment and topological localization using a sonar grid map

  • Jinwoo Choi
  • Minyong Choi
  • Sang Yep Nam
  • Wan Kyun Chung
Article

Abstract

This paper presents a method of autonomous topological modeling and localization in a home environment using only low-cost sonar sensors. The topological model is extracted from a grid map using cell decomposition and normalized graph cut. The autonomous topological modeling involves the incremental extraction of a subregion without predefining the number of subregions. A method of topological localization based on this topological model is proposed wherein a current local grid map is compared with the original grid map. The localization is accomplished by obtaining a node probability from a relative motion model and rotational invariant grid-map matching. The proposed method extracts a well-structured topological model of the environment, and the localization provides reliable node probability even when presented with sparse and uncertain sonar data. Experimental results demonstrate the performance of the proposed topological modeling and localization in a real home environment.

Keywords

Topological modeling Topological localization Sonar sensors Grid map Grid-map matching Home environment 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Beeson, P., Jong, N. K., & Kuipers, B. (2005). Towards autonomous topological place detection using the extended Voronoi graph. In Proc. of IEEE international conference on robotics and automation (pp. 4373–4379). CrossRefGoogle Scholar
  2. Brunskill, E., Kollar, T., & Roy, N. (2007). Topological mapping using spectral clustering and classification. In Proc. of IEEE/RSJ international conference on intelligent robots and systems (pp. 3491–3496). Google Scholar
  3. Buschka, P., & Saffiotti, A. (2002). A virtual sensor for room detection. In Proc. of IEEE/RSJ international conference on intelligent robots and systems (pp. 637–642). 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). CrossRefGoogle Scholar
  5. Choi, J., Choi, M., Lee, K., & Chung, W. K. (2009a). Topological modeling and classification in home environment using sonar gridmap. In Proc. of IEEE international conference on robotics and automation (pp. 3892–3898). Google Scholar
  6. Choi, J., Choi, M., & Chung, W. K. (2009b). Incremental topological modeling using sonar gridmap in home environment. In Proc. of IEEE/RSJ international conference on intelligent robots and systems (pp. 3582–3587). Google Scholar
  7. 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. CrossRefGoogle Scholar
  8. Doh, N. L., Lee, K., Chung, W. K., & Cho, H. (2009). Simultaneous localisation and mapping algorithm for topological maps with dynamics. IET Control Theory and Applications, 3(9), 1249–1260. CrossRefGoogle Scholar
  9. Elfes, A. (1989). Using occupancy grids for mobile robot perception and navigation. IEEE Computer, 22(6), 46–57. Google Scholar
  10. Gonzalez, R. C., & Woods, R. E. (2002). Digital image processing (2nd ed.). New Jersey: Prentice Hall. Google Scholar
  11. Gutmann, J.-S., & Konolige, K. (1999). Incremental mapping of large cyclic environments. In Proc. of IEEE international symposium on computational intelligence in robotics and automation (pp. 318–325). Google Scholar
  12. Katevas, N. I., Tzafestas, S. G., & Pnevmatikatos, C. G. (1998). The approximate cell decomposition with local node refinement global path planning method: Path nodes refinement and curve parametric interpolation. Journal of Intelligent and Robotic Systems, 22(3–4), 289–314. CrossRefGoogle Scholar
  13. Kleeman, L., & Kuc, R. (2008). Sonar sensing. In B. Siciliano & O. Khatib (Eds.), Handbook on robotics. Berlin: Springer. Google Scholar
  14. Lee, K., Cho, N., Chung, W. K., & Doh, N. L. (2006). Topological navigation of mobile robot in corridor environment using sonar sensor. In Proc. of IEEE/RSJ international conference on intelligent robots and systems (pp. 2760–2765). CrossRefGoogle Scholar
  15. Lee, K., & Chung, W. K. (2009). Effective maximum likelihood grid map with conflict evaluation filter using sonar sensors. IEEE Transactions on Robotics, 25(4), 887–901. CrossRefGoogle Scholar
  16. Leonard, J. J., & Durrant-Whyte, H. F. (1991). Simultaneous map building and localization for an autonomous mobile robot. In Proc. of IEEE/RSJ international conference on intelligent robots and systems (pp. 1442–1447). Google Scholar
  17. Lin, Y., Chen, C., & Wei, C. (2006). New method for subpixel image matching with rotation invariance by combining the parametric template method and the ring projection transform process. Optical Engineering, 45(6), 067 202(1-9). Google Scholar
  18. Mozos, O. M., & Burgard, W. (2006). Supervised learning of topological maps using semantic information extracted from range data. In Proc. of IEEE/RSJ international conference on intelligent robots and systems (pp. 2772–2777). CrossRefGoogle Scholar
  19. Remolina, E., & Kuipers, B. (2004). Towards a general theory of topological maps. Artificial Intelligence, 152, 47–104. CrossRefMATHMathSciNetGoogle Scholar
  20. Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888–905. CrossRefGoogle Scholar
  21. Tapus, A., & Siegwart, R. (2006). A cognitive modeling of space using fingerprints of places for mobile robot navigation. In Proc. of IEEE international conference on robotics and automation (pp. 1188–1193). Google Scholar
  22. 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 Robotic Research, 21(4), 311–330. CrossRefGoogle Scholar
  23. Thrun, S. (1998). Learning metric-topological maps for indoor mobile robot navigation. Artificial Intelligence, 99(1), 21–77. CrossRefMATHGoogle Scholar
  24. Thrun, S., Fox, D., Burgard, W., & Dellaert, F. (2001). Robust Monte Carlo localization for mobile robots. Artificial Intelligence, 128(1–2), 99–141. CrossRefMATHGoogle Scholar
  25. Thrun, S. (2002). Robotic mapping: A survey. In G. Lakemeyer, & B. Nebel (Eds.), Exploring artificial intelligence in the new millennium. San Mateo: Morgan Kaufmann. Google Scholar
  26. Yap, T. N., & Shelton, C. R. (2009). SLAM in large indoor environments with low-cost, noisy, and sparse sonars. In Proc. of IEEE international conference on robotics and automation (pp. 1395–1401). Google Scholar
  27. Yun, Y., Park, B., & Chung, W. K. (2008). Odometry calibration using home positioning function for mobile robot. In Proc. of IEEE international conference on robotics and automation (pp. 2116–2121). Google Scholar
  28. Zivkovic, Z., Bakker, B., & Krose, B. (2006). Hierarchical map building and planning based on graph partitioning. In Proc. of IEEE international conference on robotics and automation (pp. 803–809). Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Jinwoo Choi
    • 1
  • Minyong Choi
    • 1
  • Sang Yep Nam
    • 2
  • Wan Kyun Chung
    • 1
  1. 1.Dept. of Mechanical EngineeringPohang University of Science and Technology (POSTECH)PohangKorea
  2. 2.Department of Information and Communication EngineeringKookje CollegeKyeongki-doKorea

Personalised recommendations