Journal of the Korean Society for Precision Engineering (한국정밀공학회지)

Korean Society for Precision Engineering (한국정밀공학회)
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A Study on the Visual Odometer using Ground Feature Point

지면 특징점을 이용한 영상 주행기록계에 관한 연구

  • Received : 2010.01.15
  • Accepted : 2010.11.02
  • Published : 2011.03.01
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Abstract

Odometry is the critical factor to estimate the location of the robot. In the mobile robot with wheels, odometry can be performed using the information from the encoder. However, the information of location in the encoder is inaccurate because of the errors caused by the wheel's alignment or slip. In general, visual odometer has been used to compensate for the kinetic errors of robot. In case of using the visual odometry under some robot system, the kinetic analysis is required for compensation of errors, which means that the conventional visual odometry cannot be easily applied to the implementation of the other type of the robot system. In this paper, the novel visual odometry, which employs only the single camera toward the ground, is proposed. The camera is mounted at the center of the bottom of the mobile robot. Feature points of the ground image are extracted by using median filter and color contrast filter. In addition, the linear and angular vectors of the mobile robot are calculated with feature points matching, and the visual odometry is performed by using these linear and angular vectors. The proposed odometry is verified through the experimental results of driving tests using the encoder and the new visual odometry.

Keywords

References

  1. Clark, S., Dissanayake, G., Newman, P. and Durrant- Whyte, H., "A Solution to Simultaneous Localization and Map Building (SLAM) Problem," IEEE Transaction of Robotics and Automation, Vol. 17, No. 3, pp. 229-241, 2001. https://doi.org/10.1109/70.938381
  2. Doh, N., Choset, H. and Chung, W. K., "Accurate Relative Localization using Odometry," International Conference on Robotics and Automation, Vol. 2, pp. 1602-1612, 2003.
  3. Borenstein, J., "Internal Correction of Deadreckoning Errors with the Smart Encoder Trailer," International Conference on Intelligent Robots and Systems, Vol. 1, pp. 127-134, 1994.
  4. Corke, P., Strelow, D. and Singh, S., "Omnidirectional Visual Odometry for a Planetary Rover," Proc. of IROS, 2004.
  5. Campbell, J., Sukthankar, R. and Nourbakhsh, I., "Techniques for Evaluating Optical Flow for Visual Odometry in Extreme Terrain," International Conference on Intelligent Robots and Systems, Vol. 4, pp. 3704-3711, 2004.
  6. Cheng, Y., Maimone, M. W. and Matthies, L., "Visual Odometry on the Mars Exploration Rovers," IEEE International Conference on Systems, Man and Cybernetics, Vol. 1, pp. 903-910, 2005.
  7. Suhr, J. K., Jung, H. G., Bae, K. and Kim, J., "Automatic Free Parking Space Detection by using Motion Stereo-based 3D Reconstruction," Machine Vision and Applications, Vol. 21, No. 2, pp. 163-176, 2008.
  8. Cha, Y. Y. and Gweon, D. G., "An Exact 3D Data Extraction Algorithm For Active Range Sensor using Laser Slit," Journal of the Korean Society of Precision Engineering, Vol. 12, No. 8, pp. 73-85, 1995.
  9. Nuchter, A., Surmann, H. and Hertzberg, J., "Automatic Model Refinement for 3D Reconstruction with Mobile Robots," 4th International Conference on 3D Digital Imaging and Modeling, pp. 394-401, 2003.
  10. Nuchter, A., Surmann, H. and Hertzberg, J., "Automatic Classification of Objects in 3D Laser Range Scans," Proc. of 8th Conference on Intelligent Autonomous Systems, pp. 963-970, 2004.
  11. Surmann, H., Lingemann, K. and Nüchter, A., "A 3D laser range Finder for Autonomous Mobile Robots," Proc. of International Symposium on Robotics, pp. 153-158, 2001.
  12. Hahnel, D., Burgard, W. and Thrun, S., "Learning Compact 3D Models of Indoor and Outdoor Environments with a Mobile Robot," Robotics and Autonomous Systems, Vol. 44, No. 1, pp. 15-27, 2003. https://doi.org/10.1016/S0921-8890(03)00007-1
  13. Tardif, J., Pavlidis, Y. and Daniilidis, K., "Monocular Visual Odometry in Urban Environments Using an Omni-directional Camera," International Conference on Intelligent Robots and System, pp. 2531-2538, 2008.
  14. Agrawal, M. and Konolige, K., "Real-time Localization in Outdoor Environments Using Stereo Vision and Inexpensive GPS," International Conference on Pattern Recognition, pp. 1063-1068, 2006.
  15. Dornhege, C. and Kleiner, A., "Visual Odometry for Tracked Vehicles," IEEE International Workshop on Safety, Security and Rescue Robotics, 2006.
  16. Royer, E., Lhuillier, M., Dhome, M. and Lavest, J. M., "Monocular Vision for Mobile Robot Localization and Autonomous Navigation," International Journal of Computer Vision, Vol. 74, No. 3, pp. 237-260, 2007. https://doi.org/10.1007/s11263-006-0023-y
  17. Oh, Y. G., Yi, C. and Choi, B. U., "Visual Odometry using Upward Monocular Camera," Proc. of Electrical Engineering and Information Science Autumn Conference, Vol. 35, No. 2(C), pp. 457-462, 2008.
  18. Tsai, R., "A Versatile Camera Calibration Technique for High Accuracy 3D Machine Vision Metrology using off the Shelf TV Cameras and Lenses," IEEE Journal of Robotics and Automation, Vol. 3, No. 4, pp. 323-344, 1987.
  19. Faugeras, O. D. and Toscani, G., "The Calibration Problem for Stereo," IEEE Conference on Computer Vision and Pattern Recognition, pp. 15-20, 1986.