The Transactions of the Korean Institute of Electrical Engineers D (대한전기학회논문지:시스템및제어부문D)

The Korean Institute of Electrical Engineers (대한전기학회)
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Relative Localization for Mobile Robot using 3D Reconstruction of Scale-Invariant Features

스케일불변 특징의 삼차원 재구성을 통한 이동 로봇의 상대위치추정

  • 길세기 (인하대학교 전자공학과) ;
  • 이종실 (한양대학교 의공학교실) ;
  • 유제군 (한국산업기술대학교 전자공학과) ;
  • 이응혁 (한국산업기술대학교 전자공학과) ;
  • 홍승홍 (인하대학교 전자공학과) ;
  • 신동범 (인하대학교 전자공학과)
  • Published : 2006.04.01
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Abstract

A key component of autonomous navigation of intelligent home robot is localization and map building with recognized features from the environment. To validate this, accurate measurement of relative location between robot and features is essential. In this paper, we proposed relative localization algorithm based on 3D reconstruction of scale invariant features of two images which are captured from two parallel cameras. We captured two images from parallel cameras which are attached in front of robot and detect scale invariant features in each image using SIFT(scale invariant feature transform). Then, we performed matching for the two image's feature points and got the relative location using 3D reconstruction for the matched points. Stereo camera needs high precision of two camera's extrinsic and matching pixels in two camera image. Because we used two cameras which are different from stereo camera and scale invariant feature point and it's easy to setup the extrinsic parameter. Furthermore, 3D reconstruction does not need any other sensor. And the results can be simultaneously used by obstacle avoidance, map building and localization. We set 20cm the distance between two camera and capture the 3frames per second. The experimental results show :t6cm maximum error in the range of less than 2m and ${\pm}15cm$ maximum error in the range of between 2m and 4m.

Keywords

References

  1. J. Borenstein, H.R. Everett and L. Feng, Where am I? - Sensors and Methods for Mobile Robot Positioning, The University of Michigan, 1996
  2. A.J. Davison and D.W. Murry, 'Simultaneous Localization and Map-Building Using Active Vision,' IEEE Trans on PAMI, Vol. 24, No.7, July, 2002 https://doi.org/10.1109/TPAMI.2002.1017615
  3. 권인소, 장기정, '인공표식과 자연표식을 결합한 강인한 자기위치 추정', 한국정밀공학회, 춘계학술대회논문집, 2003
  4. Niklas Karlsson, et al., 'Core Technologies for Service Robotics,' Proc. of Int. Conf. on IROS2004, 2004 https://doi.org/10.1109/IROS.2004.1389862
  5. D. Lowe, 'Local feature view clustering for 3D object recognition,' Proc. of the 2001 IEEE/RSJ, Int. Conf. on Computer Vision and Pattern Recognition, Kauai, Hawaii, USA, December, 2001 https://doi.org/10.1109/CVPR.2001.990541
  6. S. Se, D. Lowe and J. Little, 'Local and global localization for mobile robots using visual landmarks,' Proc. of the 2001 IEEE/RSJ, Int. Conf. on Intelligent Robots and Systems, Maui, Hawaii, USA, October, 2001 https://doi.org/10.1109/IROS.2001.973392
  7. J. Wolf, W. Burgard and H. Burkhardt, 'Robust vision-based localization for mobile robots using an image retrievel system based on invariant features,' Proceedings of the 2002 IEEE Int. Conf. on Robotics and Automation, Washington, DC, USA, May, 2002 https://doi.org/10.1109/ROBOT.2002.1013387
  8. D. Fox, W. Burgard and S. Thrun, 'Markov localization for mobile robots in dynamic environments,' Journal of Artificial Intelligence Research 11, PP. 391-427, 1999
  9. J.S. Gutmann and K. Konolige, 'Incremental Mapping of Large Cyclic Environments,' Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), San Francisco, CA, 2000 https://doi.org/10.1109/CIRA.1999.810068
  10. S.I. Roumeliotis and G.A. Bekey, 'Bayesian estimation and Kalman filtering : A unified framework for mobile robot localization,' Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), PP. 2985-2992, San Francisco, CA, 2000 https://doi.org/10.1109/ROBOT.2000.846481
  11. S. Thrun, Mapping : A Survey, Technical Report, CMU-CS-02-111, Carnegie Mellon University, Pittsburgh, PA, USA, February, 2000
  12. K. Mikolajczyk and C. Schmid, 'A performance evaluation of local descriptors,' IEEE Conference on Computer Vision and Pattern Recognition, PP. 257-263, June 2003 https://doi.org/10.1109/CVPR.2003.1211478
  13. K. Mikolajczyk and C. Schmid, 'Indexing based on scale invariant interest points,' in Proceedings of the 8th International Conference on Computer Vision, PP. 525-531, 2000 https://doi.org/10.1109/ICCV.2001.937561
  14. G. Lowe, 'Distinctive image features from scale-invariant keypoints,' preprint, submitted to International Journal of Computer Vision, 2004 https://doi.org/10.1023/B:VISI.0000029664.99615.94
  15. M. Brown and G. Lowe, 'Invariant features from interest point groups,' In British Machine Vision Conference, Cardiff, Wales, PP. 656-665, 2002
  16. G. Lowe, 'Object recognition from local scale-invariant features,' International Conference on Computer Vision, Corfu, Greece, PP. 1150-1157, 1999 https://doi.org/10.1109/ICCV.1999.790410
  17. C. Harris and M. Stephens, 'A combined corner and edge detector,' In 4th Alvey Vision, Conference, Manchester, UK, PP. 147-151, 1988
  18. Zhengyou Zhang, 'Flexible Camera Calibration By Viewing a Plane From Unknown Orientations,' The Proceedings of the 7th IEEE International Conference on Computer Vision, Vol. 1, PP. 666-673, 1999 https://doi.org/10.1109/ICCV.1999.791289
  19. Lindeberg, Tony, 'Scale-space theory : A basic tool for analysing structures at different scales,' Journal of Applied Statistics, 21, 2(1994), PP. 224-270
  20. Kamil Wnuk, Faith Dang, and Zachary Dodds, 'Dense 3D Mapping with Monocular Vision,' Int. Conf. on Autonomous Robots and Agents, PP. 212-217, 2004