Browse > Article
http://dx.doi.org/10.5302/J.ICROS.2011.17.2.164

Monocular Vision and Odometry-Based SLAM Using Position and Orientation of Ceiling Lamps  

Hwang, Seo-Yeon (Korea University)
Song, Jae-Bok (Korea University)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.17, no.2, 2011 , pp. 164-170 More about this Journal
Abstract
This paper proposes a novel monocular vision-based SLAM (Simultaneous Localization and Mapping) method using both position and orientation information of ceiling lamps. Conventional approaches used corner or line features as landmarks in their SLAM algorithms, but these methods were often unable to achieve stable navigation due to a lack of reliable visual features on the ceiling. Since lamp features are usually placed some distances from each other in indoor environments, they can be robustly detected and used as reliable landmarks. We used both the position and orientation of a lamp feature to accurately estimate the robot pose. Its orientation is obtained by calculating the principal axis from the pixel distribution of the lamp area. Both corner and lamp features are used as landmarks in the EKF (Extended Kalman Filter) to increase the stability of the SLAM process. Experimental results show that the proposed scheme works successfully in various indoor environments.
Keywords
ceiling; mobile robot; monocular camera; SLAM;
Citations & Related Records

Times Cited By SCOPUS : 2
연도 인용수 순위
  • Reference
1 A. Davison, I. Reid, N. Molton, and O. Stasse, “MonoSLAM: real-time single camera SLAM,” IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 29, no. 6, pp. 1052-1067, June 2007.   DOI
2 P. Smith, I. Reid, and A. Davison, “Real-time monocular SLAM with straight lines,” Proc. of the 17th British Machine Vision Conference, Sep. 2006.
3 R. Newcombe and A. Davison, “Live dense reconstruction with a single moving camera,” IEEE Conf. on Computer Vision and Pattern Recognition, 2010.
4 S. Y. Hwang and J.-B. Song, “Upward monocular camera based SLAM using corner and door features,” Proc. of the IFAC 17th World Congress, pp. 1663-1668, July 2008.
5 W. Y. Jeong and K. M. Lee, “Visual SLAM with line and corner features,” Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 2570-2575, Oct. 2006.
6 E. Rosten and T. Drummond, “Machine learning for high-speed corner detection,” Proc. of European Conf. on Computer Vision, May 2006.
7 J. Matas, O. Chum, M. Urba, and T. Pajdla. “Robust wide baseline stereo from maximally stable extremal regions,” Proc. of British Machine Vision Conference, pp. 384-396, 2002.
8 명현, 정우연, 방석원, KR-B-10-0877071, 2008.
9 S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics, MIT Press, Massachusetts, 2005.
10 S. Se, D. Lowe, and J. Little, “Mobile robot localization and mapping with uncertainty using scale-invariant visual landmarks,” Int. Journal of Robotics Research, vol. 8, no. 21, pp. 735-758, Aug. 2002.
11 T. Lemaire, S. Lacroix, and J. Sola, “A practical 3D bearing-only SLAM algorithm,” Proc. of the IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 2449-2454, Aug. 2005.