Hostname: page-component-cd9895bd7-fscjk Total loading time: 0 Render date: 2024-12-24T14:08:38.359Z Has data issue: false hasContentIssue false
  • English
  • Français

Feature map management for mobile robots in dynamic environments

Published online by Cambridge University Press:  28 April 2009

Se-Jin Lee ,
Byung-Jae Park ,
Jong-Hwan Lim  and
Dong-Woo Cho
Se-Jin Lee
Affiliation:
Department of Mechanical Engineering, Pohang University of Science and Technology, San 31 Hyoja-dong, Pohang 790-784, Korea
Byung-Jae Park
Affiliation:
Department of Mechanical Engineering, Pohang University of Science and Technology, San 31 Hyoja-dong, Pohang 790-784, Korea
Jong-Hwan Lim*
Affiliation:
Department of Mechatronics, Cheju National University, #1 Ara-dong, Jeju 690-756, Korea
Dong-Woo Cho*
Affiliation:
Department of Mechanical Engineering, Pohang University of Science and Technology, San 31 Hyoja-dong, Pohang 790-784, Korea Department of Integrative Bioscience and Bioengineering, Pohang University of Science and Technology, San 31 Hyoja-dong, Pohang 790-784, Korea
*
*Corresponding authors. [email protected] and [email protected]
*Corresponding authors. [email protected] and [email protected]
Get access Rights & Permissions [Opens in a new window]

Summary

This paper presents a new approach to the management of the environmental map for mobile robots in dynamic environments. The environmental map is built of primitive features, such as lines, points, and even circles, extracted from ambiguous data captured by the robot's sonar sensor ring. The feature map must be managed because the indoor surroundings where mobile robots operate are continuously changing due to nonstationary objects, such as wastebaskets, tables, and people. The features are processed by trimming, division, or removal, depending on the dynamic circumstances. All processing refers to the occupancy probabilities of grid squares generated for the map features. The occupancy probabilities of the squares are updated using the Bayesian updating model with the sonar sensor data. Experimental results demonstrate the validity of the proposed method.

Keywords

Map managementDynamic environmentsFeature mapSonar sensor ringMobile robots
Type
Article
Information
Robotica , Volume 28 , Issue 1 , January 2010 , pp. 97 - 106
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Lim, J. H., Map Construction, Exploration, and Position Estimation for an Autonomous Mobile Robot Using Sonar Sensors Ph.D. Dissertation (Korea: Pohang Institute of Science and Technology, 1994).Google Scholar
2.Elfes, A., “Sonar-based real-world mapping and navigation,” IEEE J. Robot. Autom. RA-3 (3), 249–265 (1987).CrossRefGoogle Scholar
3.Thrun, S., Martin, C., Liu, Y., Hahnel, D., Montemerlo, R., Chakrabarti, D. and Burgard, W., “A real-time expectation-maximization algorithm for acquiring multiplanar maps of indoor environments with mobile robots,” IEEE Trans. Robot. Autom. 20, 433–442 (2004).CrossRefGoogle Scholar
4.Lim, J.-H. and Leonard, J. J., “Mobile robot relocation from echolocation constraints,” IEEE Trans. Pattern Anal. Mach. Intell. 22 (9), 1035–1041 (2000).Google Scholar
5.Lim, J.-H. and Kang, C.-U., “Grid-based localization of a mobile robot using sonar sensors,” J. Mech. Sci. Technol. 16 (3), 302–309 (2002).Google Scholar
6.Lee, S.-J., Lim, J.-H., Kang, C.-U., Chung, W.-K and Cho, D.-W., “Feature Based Map Building Using Sparse Sonar Data,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Edmonton, Alberta, Canada (August, 2005) pp. 492–496.Google Scholar
7.Lee, S.-J., Lee, Y., Yun, W.-S., Kang, C.-U., Lim, J.-H., Chung, W.-K. and Cho, D.-W., “Evaluation of Features Through Grid Association for Building a Sonar Map,” 2006 International Conference on Robots and Automation, Orlando, FL (May, 2006) pp. 2615–2620.Google Scholar
8.Wijk, O. and Christensen, H. I., “Triangulation-based fusion of sonar data with application in robot pose tracking,” IEEE Trans. Robot. Autom. 16 (6), 740–752 (2000).CrossRefGoogle Scholar
9.Crowley, J. L., “Navigation for an intelligent mobile robot,” IEEE J. Robot. Autom. RA-1 (1), 31–41 (1985).CrossRefGoogle Scholar
10.Leonard, J. J., Direct Sonar Sensing for Mobile Robot Navigation (Kluwer Academic Publishers, Dordrecht, The Netherlands, 1992).CrossRefGoogle Scholar
11.Leonard, J. J. and Durrant-Whyte, H. F., “Mobile robot localization by tracking geometric beacons,” IEEE Trans. Robot. Autom. 7 (3), 376–382 (1991).CrossRefGoogle Scholar
12.Meng, Q., Sun, Y. and Cao, Z., “Adaptive extended Kalman filter (AEKF)-based mobile robot localization using sonar,” Robotica 18, 459–473 (2000).CrossRefGoogle Scholar
13.Tardos, J. D., Neira, J., Newman, P. M. and Leonard, J. J., “Robust mapping and localization in indoor environments using sonar data,” Int. J. Robot. Res. 21 (4), 311–330 (2002).CrossRefGoogle Scholar
14.Heale, A. and Kleeman, L., “A Real Time DSP Sonar Echo Processor,” IEEE/RSJ International Conference on Intelligent Robots and Systems, Takamatsu, Japan (2000) pp. 1261–1266.Google Scholar
15.Bank, D. and Kampke, T., “High-resolution ultrasonic environment image,” IEEE Trans. Robot. 23 (2), 370–381 (2007).CrossRefGoogle Scholar
16.Fazli, S. and Kleeman, L., “Simultaneous landmark classification, localization and map building for an advanced sonar ring,” Robotica 25, 283–296 (2007).CrossRefGoogle Scholar
17.Arras, K. O., Castellanos, J. A., Schilt, M. and Siegwart, R., “Feature-based multi-hypothesis localization and tracking using geometric constraints,” Robot. Auton. Syst. 1056, 1–13 (2003).Google Scholar
18.Wolf, F. D. and Sukhatme, G. S., “Mobile robot simultaneous localization and mapping in dynamic environments,” Auton. Robots 19, 53–65 (2005).CrossRefGoogle Scholar
19.Davison, A. J., “Real-time simultaneous localization and mapping with a single camera,” IEEE Int. Conf. Comput. Vis. 2, 1403–1410 (2003).Google Scholar
20.Lee, K. H., Kim, S. H. and Kwak, Y. K., “Mobility improvement of an Internet-based robot system using the position prediction simulator,” Int. J. Precis. Eng. Manuf. 6 (3), 29–36 (2005).Google Scholar
21.Lee, S.-J., Lim, J.-H. and Cho, D.-W., “Feature-based map building using sparse sonar data in a home-like environment,” J. Mech. Sci. Technol. 21 (1), 74–82 (2007).CrossRefGoogle Scholar
22.Lee, S.-J., Lim, J.-H. and Cho, D.-W., “Development of robust feature detector using sonar data,” J. Korean Soc. Precis. Eng. 25 (2), 35–42 (2008).Google Scholar
23.Dissanayake, G., Williams, S. B., Durrant-Whyte, H. and Bailey, T., “Map management for efficient simultaneous localization and mapping (SLAM)”, Auton. Robots 12, 267–286 (2002).CrossRefGoogle Scholar
24.Thrun, S., Burgard, W. and Fox, F., Probabilistic Robotics (MIT Press. Cambridge, MA, 2005).Google Scholar