International Journal of Control, Automation, and Systems

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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A Simple Framework for Indoor Monocular SLAM

  • Published : 2008.02.28
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Abstract

Vision-based simultaneous localization and map building using a single camera, while compelling in theory, have not until recently been considered extensive in the practical realm of the real world. In this paper, we propose a simple framework for the monocular SLAM of an indoor mobile robot using natural line features. Our focus in this paper is on presenting a novel approach for modeling the landmark before integration in monocular SLAM. We also discuss data association improvement in a particle filter approach by using the feature management scheme. In addition, we take constraints between features in the environment into account for reducing estimated errors and thereby improve performance. Our experimental results demonstrate the feasibility of the proposed SLAM algorithm in real-time.

Keywords

References

  1. D. O. Gorodnichy and W. W. Armstrong, "Single camera stereo for mobile robot world exploration," Proc. of Vision Interface Conf. VI, 1999
  2. S. Thrun, A. Buecken, W. Burgard, D. Fox, T. Froehlinghaus, D. Henning, T. Hofmann, M. Krell, and T. Schmidt, "Map learning and highspeed navigation in RHINO," AI-based Mobile Robots: Case Studies of Successful Robot Systems, D. Kortenkamp, R. P. Bonasso, and R. Murphy, ed., MIT Press, 1998
  3. D. Murray and J. Little, "Using real-time stereo vision for mobile robot navigation," Proc. of IEEE Conf. on Computer Vision and Pattern Recognition, 1998
  4. W. Y. Jeong and K. M. Lee, "Visual SLAM with line and corner features," Proc. of the IEEE/RSJ International Conference on Intelligent Robotics and System, Beijing, China, October 2006
  5. J. Folkesson, P. Jensfelt, and H. I. Christensen, "Vision SLAM in the measurement subspace," Proc. of the IEEE International Conference on Robotics and Automation, 2005
  6. J. M. M. Montiel, J. Civera, and A. J. Davison, "Unified inverse depth parametrization for monocular SLAM," Proc. of Robotics: Science and Systems, Philadelphia, 2006
  7. E. Eade and T. Drummond, "Edge landmarks in monocular slam," Proc. of British Machine Vision Conference, 2006
  8. P. Smith, I. Reid, and A. Davison, "Real-time monocular slam with straight lines," Proc. of British Machine Vision Conference, 2006
  9. E. Eade and T. Drummond, "Scalable monocular slam," Proc. of IEEE Conf. Computer Vision and Pattern Recognition, 2006
  10. T. Lemaire and S. Lacroix. "Monocular-Vision based SLAM using line segments," Proc of Robotic 3D Environment Cognition, Workshop at the International Conference Spatial Cognition, 2006
  11. P. Elinas, R. Sim, and J. J. Little, "${\sigma}$SLAM: Stereo vision SLAM using the Rao-Blackwellised particle filter and a novel mixture proposal distribution," Proc. of the IEEE International Conference on Robotics and Automation, 2006
  12. R. Sim, P. Elinas, M. Griffin, A. Shyr, and J. J. Little, "Design and analysis of a framework for real-time vision-based SLAM using Rao-Blackwellised particle filters," Proc. of the IEEE International Conference on Robotics and Automation, Orlando, Florida, May 2006
  13. M. Li, B. Hong, Z. Cai, and R. Luo, "Novel Rao-Blackwellized particle filter for mobile robot SLAM using monocular vision," International Journal of Intelligent Technology, vol. 1, no. 1, 2006
  14. J. Sola, A. Monin, M. Devy, and T. Lemaire, "Undelayed initialization in bearing only SLAM," Proc. of the IEEE International Conference on Robotics and Automation, 2005
  15. A. J. Davison, "Real-time simultaneous localization and mapping with a single camera," Proc. of International Conference on Computer Vision, 2003
  16. T. Lemaire and S. Lacroix, "Monocular-vision based SLAM using line segments," Proc. of the IEEE International Conference on Robotics and Automation, 2007
  17. E. Eade and T. Drummond, "Scalable monocular SLAM," Proc. of Conference on Computer Vision and Pattern Recognition, New York, USA, pp. 469-468, 2006
  18. N. M. Kwok and G. Dissanayake, "Bearing-only SLAM in indoor environments using a modified particle filter," Proc. of the Australasian Conference on Robotics & Automation, 2003
  19. M. Pupilli and A. Calway, "Real-time camera tracking using a particle filter," Proc. of British Machine Vision Conference, 2005
  20. S. Baker and I. Matthews, "Lucas-Kanade 20 years on: A unifying framework: Part 1," http://citeseer.nj.nec.com/531560.html, 2002
  21. V. Lepetit and P. Fua, "Monocular model-based 3d tracking of rigid objects: A survey," Foundations and Trends in Computer Graphics and Vision, vol. 1, no. 1, 2005
  22. S. J. Julier, "The scaled unscented transformation," http://www.cs.unc.edu/welch/kalman/media/pdf/ACC02-IEEE1357.PDF
  23. M. Montemerlo, S. Thrun, D. Koller, and B.Wegbreit, "FastSLAM: A factored solution to the simultaneous localization and mapping problem," Proc. of the AAAI National Conf. on Artificial Intelligence, Edmonton, Canada, AAAI, 2002
  24. W. Wen and H. Durrant-Whyte, "Model-based multi-sensor data fusion," Proc. of IEEE Intl. Conf. on Robotics and Automation, pp. 1720-1726, 1992
  25. D. Rodriguez-Losada, F. Matia, A. Jimenez, and R. Galan, "Consistency improvement for SLAM -EKF for indoor environments," Proc. of IEEE Intl. Conf. on Robotics and Automation, pp. 418-423, 2006
  26. K. R. Beevers, W. H. Huang, "Inferring and enforcing relative constraints in SLAM," Proc. of the 7th Intl. Workshop on the Algorithmic Foundations of Robotics, New York, USA, July 16-18, 2006
  27. G. Grisetti, C. Stachniss, and W. Burgard, "Improving gridbased SLAM with Rao-Blackwellized particle filters by adaptive proposals and selective resampling," Proc. of the IEEE International Conference on Robotics and Automation, Barcelona, Spain, April 2005