Mobile robot localization based on effective combination of vision and range sensors
- 197 Downloads
Most localization algorithms are either range-based or vision-based, but the use of only one type of sensor cannot often ensure successful localization. This paper proposes a particle filter-based localization method that combines the range information obtained from a low-cost IR scanner with the SIFT-based visual information obtained from a monocular camera to robustly estimate the robot pose. The rough estimation of the robot pose by the range sensor can be compensated by the visual information given by the camera and the slow visual object recognition can be overcome by the frequent updates of the range information. Although the bandwidths of the two sensors are different, they can be synchronized by using the encoder information of the mobile robot. Therefore, all data from both sensors are used to estimate the robot pose without time delay and the samples used for estimating the robot pose converge faster than those from either range-based or vision-based localization. This paper also suggests a method for evaluating the state of localization based on the normalized probability of a vision sensor model. Various experiments show that the proposed algorithm can reliably estimate the robot pose in various indoor environments and can recover the robot pose upon incorrect localization.
KeywordsMobile robot localization sensor fusion sensor model vision-based navigation
Unable to display preview. Download preview PDF.
- Y. J. Lee, T. B. Kwon, and J. B. Song, “SLAM of a Mobile Robot using Thinning-based Topological Information,” International Journal of Control, Automation, and Systems, vol. 5, no. 5, pp. 577–583, October 2007.Google Scholar
- J. Kosecka and F. Li, “Vision based topological Markov localization,” Proc. of IEEE Int’l Conf. on Robotics and Automation, pp. 1481–1486, 2004.Google Scholar
- X. D. Nguyen, B. J. You, and S. R. Oh, “A simple framework for indoor monocular SLAM,” International Journal of Control, Automation, and Systems, vol. 6, no. 1, pp. 62–75, February 2008.Google Scholar
- W. Shang and D. Sun, “Multi-sensory fusion for mobile robot self-localization,” Proc. of IEEE International Mechatronics and Automation, pp. 871–876, 2006.Google Scholar
- S. Thimpson and S. Kagami, “Stereo Vision and Sonar Sensor Based View Registration for 2.5 Dimensional Map Generation,” Proc. of IEEE Intl. Conf. on Intelligent Robots and Systems, pp. 3444–3449, 2004.Google Scholar
- D. Fox, W. Burgard, F. Dellaert, and S. Thrun, “Monte Carlo Localization: Efficient Position Estimation for Mobile Robots,” Proc. of the Sixteenth National Conference on Artificial Intelligence, pp. 343–349, 1999.Google Scholar
- M. Alwan, M. B. Wagner, G. Wasson, and P. Sheth, “Characterization of Infrared Range-Finder PBS-03JN for 2-D Mapping,” Proc. of IEEE Int. Conference on Robotics and Automation, pp. 3936–3941, 2004.Google Scholar
- S. Thrun, W. Burgard and D. Fox, Probability Robotics, MIT Press, 2005.Google Scholar
- A. Torralba, K. P. Murphy, W. T. Freeman, and M. A. Rubin, “Context-based vision system for place and object recognition,” Proc. of Int. Conference on Computer Vision, pp. 273–280, 2003.Google Scholar