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Development of sonar morphology-based posterior approach model for occupancy grid mapping

Published online by Cambridge University Press:  06 March 2015

Se-Jin Lee,
Kyoungmin Lee and
Jae-Bok Song
Se-Jin Lee
Affiliation:
Division of Mechanical and Automotive Engineering, Kongju National University, 1223-24 Cheonan-daero, Seobuk-gu, Cheon an-si, Chungcheongnam-do, 330-710, Republic of Korea
Kyoungmin Lee
Affiliation:
National Research Council(NRC) Research Associate, Naval Postgraduate School, 1 University Circle, Monterey, CA 93943, USA
Jae-Bok Song*
Affiliation:
School of Mechanical Engineering, Korea University, 5, Anam-dong, Seongbuk-gu, Seoul, 136-713, Republic of Korea
*
*Corresponding author. E-mail: jbsong@korea.ac.kr
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Summary

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An advanced sonar morphology-based posterior approach (SMP) to building an occupancy grid map for a mobile robot is proposed in this study. It is very important for a mobile robot to find its surrounding saliencies and to localize itself for indoor navigation. Ultrasonic sensors are of great practical value in building environmental maps and for autonomous operation. However, grid maps constructed by ultrasonic sensors cannot typically form a realistic representation of a given environment due to incorrect sonar measurements caused by specular reflection and wide beam width. The sonar sensor model proposed in this study, in which the negative effect of incorrect sonar measurements is minimized by geometric association with sonar footprints, is adopted to build a high-quality grid map. Experimental results and evaluations in home and corridor environments demonstrate the validity of the proposed methods.

Keywords

Mobile robotsRobot mappingOccupancy gridSonar sensorsRobot localization

Information

Type
Articles
Information
Robotica , Volume 35 , Issue 1 , January 2017, pp. 73 - 84
Copyright
Copyright © Cambridge University Press 2015 

References

1. Lee, S.-J., Park, B.-J., Lim, J.-H. and Cho, D.-W., “Feature map management for mobile robots in dynamic environments,” Robotica 28 (1), 97–106 (2010).Google Scholar
2. Lim, J.-H. and Cho, D.-W., “Sonar based systematic exploration method for an autonomous mobile robot operating in an unknown environment,” Robotica 16, 659–667 (1998).Google Scholar
3. Lee, S.-J., Lim, J.-H. and Cho, D.-W., “General feature extraction for mapping and localization of a mobile robot using sparsely sampled sonar data,” Adv. Robot. 23 (12–13), 1601–1616 (2009).Google Scholar
4. Lee, S.-J., Lim, J.-H., Cho, D.-W. and Song, J.-B., “Novel sonar salient feature structure for extended kalman filter-based simultaneous localization and mapping of mobile robots,” Adv. Robot. 26 (8–9), 1055–1074 (2012).CrossRefGoogle Scholar
5. Lee, K. and Chung, W. K., “Effective maximum likelihood grid map with conflict evaluation filter using sonar sensors,” IEEE Trans. Robot. 25 (4), 887–901 (2009).Google Scholar
6. Habib, M. K., “Real time mapping and dynamic navigation for mobile robots,” Int. J. Adv. Robot. Syst. 4 (3), 323–338 (2007).Google Scholar
7. Thrun, S., Burgard, W. and Fox, D., Probabilistic Robotics (MIT Press, Cambridge, 2002).Google Scholar
8. Ribo, M. and Pinz, A., “A comparison of three uncertainty calculi for building sonar-based occupancy grids,” Robot. Auton. Syst. 35, 201–209 (2001).Google Scholar
9. Oriolo, G., Ulivi, G. and Vendittelli, M., “Fuzzy maps: A new tool for mobile robot perception and planning,” J. Robot. Syst. 14 (3), 179–197 (1997).Google Scholar
10. Pagac, D., Nebot, E. M. and Durrant-Whyte, H. F., “An evidential approach to map-building for autonomous vehicles,” IEEE Trans. Robot. Autom. 14 (2), 623–629 (1998).Google Scholar
11. Murphy, R. R., “Dempster-shafer theory for sensor fusion in autonomous mobile robots,” IEEE Trans. Robot. Autom. 14 (2), 197–206 (1998).Google Scholar
12. Thrun, S., “Learning ocupancy grid maps with forward sensor models,” Auton. Robot. 15, 111–127 (2003).Google Scholar
13. Pathak, K., Birk, A., Poppinga, J. and Schwertfeger, S., “3D Forward Sensor Modeling and Application to Occupancy Grid based Sensor Fusion,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robotics and Systems, (San Diego, CA, 2007) pp. 2059–2064.Google Scholar
14. Lee, K., Lee, S.-J., Kolsch, M. and Chung, W. K., “Enhanced maximum likelihood grid map with reprocessing incorrect sonar measurements,” Auton. Robot. 35, 123–141 (2013).Google Scholar
15. Burguera, A., Gonzalez, Y. and Oliver, G., “Sonar Scan Matching by Filtering Scans using Grids of Normal Distributions,” Proceedings of International Conference on Intelligent Autonomous Systems, (2008) pp. 64–73.Google Scholar
16. Kuc, R. and Siegel, M., “Physically-based simulation model for acoustic sensor robot navigation,” IEEE Trans. Pattern Anal. Mach. Intell. 9 (6), 766–778 (1978).Google Scholar
17. Leonard, J. J. and Durrant-Whyte, H. F., Directed Sonar Sensing for Mobile Robot Navigation (Kluwer Academic, Boston, 1992).Google Scholar
18. O'Sullivan, S., Collins, J. J., Mansfield, M., Haskett, D. and Eaton, M., “Linear Feature Prediction for Confidence Estimation of Sonar Readings in Map Building,” Proceedings of the International Symposium on Artificial Life and Robotics, (2004).Google Scholar
19. Lee, K. and Chung, W. K., “Navigable Voronoi Diagram: A Local Path Planner for Mobile Robots using Sonar Sensors,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robotics and Systems, (2007) pp. 2813–2818.Google Scholar
20. Burguera, A., Gonzalez, Y. and Oliver, G., “Probabilistic Sonar Filtering in Scan Matching Localization,” Proceedings of the IEEE/RSJ International Conference on Intelligent Robotics and Systems, (2007) pp. 4158–4163.Google Scholar
21. Van Rijsbergen, C. J., Information Retrieval, 2nd ed. (Butterworth, London, 1979).Google Scholar
22. Fairfield, N. and Wettergreen, D., “Evidence Grid-Based Methods for 3D Map Matching,” Proceedings of the IEEE International Conference on Robotics and Automation, (2009) pp. 1637–1642.Google Scholar
23. Ulrich, I. and Borenstein, J., “VFH*: Local Obstacle Avoidance with Look-Ahead Verification,” Proceedings of the IEEE International Conference on Robotics and Automation, (2000) pp. 2505–2511.Google Scholar