Autonomous Robots

, Volume 35, Issue 2–3, pp 123–141 | Cite as

Enhanced maximum likelihood grid map with reprocessing incorrect sonar measurements

  • Kyoungmin Lee
  • Se-Jin Lee
  • Mathias Kölsch
  • Wan Kyun Chung
Article

Abstract

In this paper, we address the problem of building a grid map as accurately as possible using inexpensive and error-prone sonar sensors. In this research area, incorrect sonar measurements, which fail to detect the nearest obstacle in their beamwidth, generally have been dealt with in the same manner as correct measurements or have been excluded from the mapping. In the former case, the map quality may be severely degraded. In the latter case, the resulting map may have insufficient information after the incorrect measurements are removed because only correct measurements are frequently insufficient to cover the whole environment. We propose an efficient grid-mapping approach that incorporates incorrect measurements in a specialized manner to build a better map; we call this the enhanced maximum likelihood (eML) approach. The eML approach fuses the correct and incorrect measurements into a map based on sub-maps generated from each set of measurements. We also propose the maximal sound pressure (mSP) method to detect incorrect sonar readings using the sound pressure of the waves from sonar sensors. In several indoor experiments, integrating the eML approach with the mSP method achieved the best results in terms of map quality among various mapping approaches. We call this the maximum likelihood based on sub-maps (MLS) approach. The MLS map created using only two sonar sensors exhibited similar accuracy to the reference map, which was an accurate representation of the environment.

Keywords

Grid mapping Sonar sensor Maximum likelihood estimation 

Supplementary material

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References

  1. Ahn, S., Choi, J., Doh, N. L., & Chung, W. K. (2008). A practical approach for ekf-slam in an indoor environment: Fusing ultrasonic sensors and stereo camera. Autonomous Robots, 24(3), 315–335.CrossRefGoogle Scholar
  2. Barshan, B. (2007). Directional processing of ultrasonic arc maps and its comparison with existing techniques. International Journal of Robotics Research, 26(8), 797–820.CrossRefGoogle Scholar
  3. Bishop, C. M. (2007). Pattern recognition and machine learning. Berlin: Springer.Google Scholar
  4. Burguera, A., Gonzalez, Y., & Oliver, G. (2007). Probabilistic sonar filtering in scan matching localization (pp. 4158–4163). In: Proceedings of the IEEE/RSJ international Conference on intelligent robotics and systems.Google Scholar
  5. Burguera, A., Gonzalez, Y., & Oliver, G. (2008). Sonar scan matching by filtering scans using grids of normal distributions (pp. 64–73). In: International Conference on intelligent autonomous systems.Google Scholar
  6. Burguera, A., Gonzalez, Y., & Oliver, G. (2009a). On the use of likelihood fields to perform sonar scan matching localization. Autonomous Robots, 26, 203–222.CrossRefGoogle Scholar
  7. Burguera, A., Gonzalez, Y., & Oliver, G. (2009b). Sonar sensor models and their application to mobile robot localization. Sensors (MDPI), 9(12), 10217–10243.CrossRefGoogle Scholar
  8. Carlson, J., Murphy, R. R., Christopher, S., & Casper, J. (2005). Conflict metric as a measure of sensing quality (pp. 2032–2039). In: Proceedings of the IEEE international conference on robotics and automation.Google Scholar
  9. Choi, J., Choi, M., Nam, S. Y., & Chung, W. K. (2011). Autonomous topological modeling of a home environment and topological localization using a sonar grid map. Autonomous Robots, 30, 351–368.CrossRefGoogle Scholar
  10. Choset, H., Nagatani, K., & Lazar, N. (2003). The arc-transversal median algorithm: A geometric approach to increasing ultrasonic sensor azimuth accuracy. IEEE Transactions on Robotics and Automation, 19(3), 513–521.CrossRefGoogle Scholar
  11. Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via em algorithm. Journal of the Royal Statistical Society Series B (Methodological), 39(1), 1–38.MathSciNetMATHGoogle Scholar
  12. 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.MathSciNetCrossRefGoogle Scholar
  13. Hebert, M. (2000). Active and passive range sensing for robotics (pp. 102–110). In: Proceedings of the IEEE international conference on robotics and automation.Google Scholar
  14. Ivanjko, E., Petrovic, I., Macek, K., & (2003) Improvements of occupancy grid maps by sonar data corrections. In: Proceedings of FIRA Robot Soccer world congress. Vienna: Austria.Google Scholar
  15. Kleeman, L., & Kuc, R. (2008). Sonar sensing. In O. Khatib & B. Siciliano (Eds.), Handbook of robotics. Berlin: Springer.Google Scholar
  16. Kuc, R., & Siegel, M. (1987). Physically-based simulation model for acoustic sensor robot navigation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 9(6), 766–778.CrossRefGoogle Scholar
  17. Lee, J. S., & Chung, W. K. (2010). Robust mobile robot localization in highly non-static environments. Autonomous Robots, 29, 1–16.CrossRefGoogle Scholar
  18. Lee, K., & Chung, W. K. (2007). Navigable voronoi diagram: a local path planner for mobile robots using sonar sensors (pp. 2813–2818). In: Proceedings of the IEEE/RSJ international conference on intelligent robotics and systems.Google Scholar
  19. 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
  20. Leonard, J. J., & Durrant-Whyte, H. F. (1992). Directed sonar sensing for mobile robot navigation. Boston: Kluwer Academic.MATHCrossRefGoogle Scholar
  21. Moravec, H. P. (1988). Sensor fusion in certainty grids for mobile robots. AI Magazine, 9(2), 61–74.Google Scholar
  22. Murphy, R. R. (1998). Dempster-shafer theory for sensor fusion in autonomous mobile robots. IEEE Transactions on Robotics and Automation, 14(2), 197–206.CrossRefGoogle Scholar
  23. Noykov, S., & Roumenin, C. (2007). Occupancy grids building by sonar and mobile robot. Robotics and Autonomous Systems, 55(2), 162–175.CrossRefGoogle Scholar
  24. Oriolo, G., Ulivi, G., & Vendittelli, M. (1997). Fuzzy maps: A new tool for mobile robot perception and planning. Journal of Robotic Systems, 14(3), 179–197.CrossRefGoogle Scholar
  25. Oriolo, G., Ulivi, G., & Vendittelli, M. (1998). Real-time map building and navigation for autonomous robots in unknown environments. IEEE Transactions on Systems Man and Cybernetics Part B Cybernetics, 28(3), 316–333.CrossRefGoogle Scholar
  26. O’Sullivan, S., Collins, J. J., Mansfield, M., Haskett, D., & Eaton, M. (2004). Linear feature prediction for confidence estimation of sonar readings in map building. In: Proceedings of the international symposium on artificial life and robotics.Google Scholar
  27. Pagac, D., Nebot, E. M., & Durrant-Whyte, H. F. (1998). An evidential approach to map-building for autonomous vehicles. IEEE Transactions on Robotics and Automation, 14(2), 623–629.CrossRefGoogle Scholar
  28. Pathak, K., Birk, A., Poppinga, J., & Schwertfeger, S. (2007). 3d forward sensor modeling and application to occupancy grid based sensor fusion (pp. 2059–2064). In: Proceedings of the IEEE/RSJ international conference on intelligent robotics and systems.Google Scholar
  29. Ribo, M., & Pinz, A. (2001). A comparison of three uncertainty calculi for building sonar-based occupancy grids. Robotics and Autonomous Systems, 35, 201–209.MATHCrossRefGoogle Scholar
  30. Shafer, G. (1976). A mathematical theory of evidence. Princeton: Princeton University Press.MATHGoogle Scholar
  31. Silver, D., Morales, D., Rekleitis, L., Lisien, B., & Choset, H. (2004). Arc carving : Obtaining accurate, low latency maps from ultrasonic range sensors (pp. 1554–1561). In: Proceedings of the IEEE international conference on robotics and automation.Google Scholar
  32. Thrun, S. (1998). Learning metric-topological maps for indoor mobile robot navigation. Artificial Intelligence, 99(1), 21–71.MATHCrossRefGoogle Scholar
  33. Thrun, S. (2003). Learning ocupancy grid maps with forward sensor models. Autonomous Robots, 15, 111–127.CrossRefGoogle Scholar
  34. Thrun, S., Burgard, W., & Fox, D. (2002). Probabilistic robotics. Cambridge: MIT Press.Google Scholar
  35. Zadeh, L. A. (1973). Outline of a new approach to the analysis of complex systems and decision process. IEEE Transactions on Systems Man and Cybernetics, 3, 28–44.MathSciNetMATHCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Kyoungmin Lee
    • 1
  • Se-Jin Lee
    • 2
  • Mathias Kölsch
    • 1
  • Wan Kyun Chung
    • 3
  1. 1.Department of Computer ScienceNaval Postgraduate SchoolMontereyUSA
  2. 2.Kyungil UniversityGyeongsanKorea
  3. 3.Department of Mechanical EngineeringPohang University of Science and Technology (POSTECH)PohangKorea

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