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Multipath Bayesian Map Construction Model from Sonar Data

Published online by Cambridge University Press:  09 March 2009

Jong Hwan Lim  and
Dong Woo Chof†
Jong Hwan Lim
Affiliation:
Assistant Professor, Dept. of Mech. Eng., Cheju National University.
Dong Woo Chof†
Affiliation:
Associate Professor, Dept. of Mech., Pohang University of Science and Technology, San 31 Hyoja-dong, Pohang 790-784 (Korea)
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Summary

A new model for the construction of a sonar map in a specular environment has been developed and implemented. In a real world, where most of the object surfaces are specular ones, a sonar sensor surfers from a multipath effect which results in a wrong interpretation of an object's location. To reduce this effect and hence to construct a reliable map of a robot's surroundings, a probabilistic approach based on Bayesian reasoning is adopted to both evaluation of object orientations and estimation of an occupancy probability of a cell by an object. The usefulness of this approach is illustrated with the results produced by our mobile robot equipped with ultrasonic sensors.

Keywords

Map constructionSonar dataMultipath effectsBayesian reasoningMobile robot.
Type
Article
Information
Robotica , Volume 14 , Issue 5 , September 1996 , pp. 527 - 540
Copyright
Copyright © Cambridge University Press 1996

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References

1. Moravec, H.P. and Elfes, A., “High Resolution Maps from Wide Angle Sonar” IEEE International Conference on Robotics and Automation,St. Louis(1985) pp. 116–121.Google Scholar
2. Moravec, H.P., “Sensor Fusion in Certainty Grids for Mobile Robots” AI Magazine 9, No. 2, 61–74 (1988).Google Scholar
3. Cho, D.W. and Moravec, H.P, “A Bayesian Method for Certainty Grids” AAAl Spring Symposium on Robot Navigation, Stanford CA (1989) pp. 57–60.Google Scholar
4. Cho, D.W., “Certainty Grid Representation for Robot Navigation by a Bayesian Method” Robotica 8, Part 2, 159–165 (1990).CrossRefGoogle Scholar
5. Lim, J.H. and Cho, D.W., “Specular Reflection Probability in the Certainty Grid Representation” Transactions of the ASME, Journal of Dynamic Systems, Measurement and Control 116, No. 3, 512–520 (1994).CrossRefGoogle Scholar
6. Lim, J.H. and Cho, D.W., “Physically Based Sensor Modeling for a Sonar Map in a Specular Environment” IEEE International Conference on Robotics and Automation,Nice, France(May 12–14, 1992) pp. 1714–1719.Google Scholar
7. Elfes, A., “Sonar-Based Real-World Mapping and Navigation” IEEE Transactions on Robotics an Automation RA-3, No. 3, 249–265 (1987).Google Scholar
8. Borenstein, J. and Koren, Y., “The Vector Field Histogram-Fast Obstacle Avoidance for Mobile Robots” IEEE Transactions on Robotics and Automation 7, No. 3, 278–288 (1991).Google Scholar
9. Borenstein, J. and Koren, Y., “Histogramic In-Motion Mapping for Mobile Robot Obstacle Avoidance” IEEE Transactions on Robotics and Automation 7, No. 4, 535–539 (1991).Google Scholar
10. Kuc, R. and Siegel, M.W., “Physical based Simulation Model for Acoustic Sensor Robot Navigation” IEEE Transactions on Pattern Analysis and Machine Intelligence 9, NO. 6, 766–778 (1987).Google ScholarPubMed
11. Brasan, B. and Kuc, R., “Differentiating Sonar Reflections from Corners and Planes Employing an Intelligent sensor” IEEE Transactions on Pattern Analysis and Machine Intelligence 12, No. 6, 560–569 (1990).Google Scholar
12. Kuc, R. and Viard, V.B., “A Physical Based Navigation Strategy for Sonar-Guided Vehicles” Int. J. Robotics Research 10, No. 2, 75–87 (1991).CrossRefGoogle Scholar
13. Bozma, and Kuc, R., “Building a Sonar Map in a Specular Environment Using a Single Mobile Sensor” IEEE Transactions on Pattern Analysis and Machine Intelligence 13, No. 12, 1260–1269 (1991).Google Scholar
14. Walter, S.A., “The Sonar Ring: Obstacle Detection for a Mobile Robot” IEEE International Conference on Robotics and Automation,Raleigh North CarolinaMarch 31-April 3, 1987 (1987) pp. 1574–1579.Google Scholar
15. Carlin, B., Ultrasonics (McGraw-Hill, New York, NY, 1980).Google Scholar
16. Nayar, S.K., Ikeuchi, K., Kanade, T., “Surface Reflection: Physical and Geometrical Perspectives” Proc. of Image Understanding Workshop September11–13 1990 (1990) pp. 185–212.Google Scholar
17. Berger, J.O., Statistical Decision Theory and Bayesian Analysis (Springer-Verlag, New York, 1985).Google Scholar
18. Polaroid Corporation, Ultrasonic Range Finders (Polaroid, Cambridge, Mass., 1982).Google Scholar