The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
권호 표지
DOI QR코드

DOI QR Code

Motion Estimation Using 3-D Straight Lines

3차원 직선을 이용한 카메라 모션 추정

  • Lee, Jin Han (jin Han Lee is with the Department of Electronics and Computer Engineering, Hanyang University) ;
  • Zhang, Guoxuan (Guoxuan Zhang is with the Department of Electronics and Computer Engineering, Hanyang University) ;
  • Suh, Il Hong (Department of Electronics and Computer Engineering, Hanyang University)
  • Received : 2016.10.27
  • Accepted : 2016.11.22
  • Published : 2016.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes a method for motion estimation of consecutive cameras using 3-D straight lines. The motion estimation algorithm uses two non-parallel 3-D line correspondences to quickly establish an initial guess for the relative pose of adjacent frames, which requires less correspondences than that of current approaches requiring three correspondences when using 3-D points or 3-D planes. The estimated motion is further refined by a nonlinear optimization technique with inlier correspondences for higher accuracy. Since there is no dominant line representation in 3-D space, we simulate two line representations, which can be thought as mainly adopted methods in the field, and verify one as the best choice from the simulation results. We also propose a simple but effective 3-D line fitting algorithm considering the fact that the variance arises in the projective directions thus can be reduced to 2-D fitting problem. We provide experimental results of the proposed motion estimation system comparing with state-of-the-art algorithms using an open benchmark dataset.

Keywords

References

  1. P.J. Besl and N.D. McKay, "A method for registration of 3-D shapes," IEEE Trans. Pattern Anal. Mach. Intell. (PAMI), vol.14, no.2, pp.239-256, February, 1992. https://doi.org/10.1109/34.121791
  2. P. Henry, M. Krainin, E. Herbst, X. Ren, and D. Fox, "RGB-D mapping: Using depth cameras for dense 3D modeling of indoor environments," in International Symposium on Experimental Robotics (ISER), 2010.
  3. H. Strasdat, A. Davison, J. Montiel, and K. Konolige, "Double Window Optimisation for Constant Time Visual SLAM," in IEEE International Conference on Computer Vision (ICCV), 2011.
  4. J. Weingarten and R. Siegwart, "3D SLAM using planar segments," in IEEE/RSJ International Conference of Intelligent Robots and Systems (IROS), pp.3062-3067, 2006.
  5. K. Pathak, A. Birk, N. Vaskevicius, and J. Poppinga, "Fast registration based on noisy planes with unknown correspondences for 3-D mapping," IEEE Trans. Robotics (T-RO), vol.26, no.3, pp.424-441, June, 2010. https://doi.org/10.1109/TRO.2010.2042989
  6. Y. Taguchi, Y. Jian, S. Ramalingam, and C. Feng, "Point-Plane SLAM for Hand-Held 3D Sensors," in IEEE International Conference on Robotics and Automation (ICRA), 2013.
  7. J.H. Lee, G. Zhang, J. Lim, and I.H. Suh, "Place Recognition using Straight Lines for Vision-based SLAM," in IEEE International Conference on Robotics and Automation (ICRA), 2013.
  8. B.K.P. Horn, "Closed-form solution of absolute orientation using unit quaternions," Journal of Optical Society of America A, vol.4, no.4, pp.629-642, 1987.
  9. K. Kanatani, "Unbiased estimation and statistical analysis of 3-D rigid motion from two views," IEEE Trans. Pattern Anal. Mach. Intell. (PAMI), vol.15, no.1, pp.37-50, June, 1993. https://doi.org/10.1109/34.184773
  10. K. Levenberg, "A method for the solution of certain nonlinear problems in least squares," Quarterly of Applied Mathematics, vol.2, no.2, pp.164-168, 1944. https://doi.org/10.1090/qam/10666
  11. D. Marquardt, "An algorithm for least-squares estimation of nonlinear parameters," Journal of the Society for Industrial and Applied Mathematics, vol.11, no.2, pp.431-441, 1963. https://doi.org/10.1137/0111030
  12. S. Agarwal and K. Mierle, "Ceres Solver: Tutorial & Reference," Google Inc., http://code.google.com/p/ceressolver/.
  13. J. Sturm, W. Burgard, and D. Cremers, "Evaluating Egomotion and Structure-from-Motion Approaches Using the TUM RGB-D Benchmark," in IEEE/RJS International Conference on Intelligent Robot (IROS), 2012.
  14. Zhiheng WANG, Fuchao WU, Zhanyi HU, "MSLD: A Robust Descriptor for Line Matchingm," Pattern Recognition, vol.42, no.5, pp.941-953, 2009. https://doi.org/10.1016/j.patcog.2008.08.035
  15. AS Huang and Abraham Bachrach, "Visual odometry and mapping for autonomous flight using an RGB-D camera," in International Symposium on Robotics Research, pp.1-16, 2011.
  16. Christian Kerl, Jurgen Sturm, and Daniel Cremers, "Robust Odometry Estimation for RGB-D cameras," In IEEE International Conference on Robotics and Automation (ICRA), pp.3748-3754, May, 2013.
  17. Henrik Andreasson and Todor Stoyanov, "Real Time Registration of RGB-D Data Using Local Visual Features and 3D-NDT Registration," in IEEE International Conference on Robotics and Automation (ICRA) Workshop on Semantic Perception, Mapping and Exploration (SPME), 2012.
  18. Ivan Dryanovski and Roberto G. Valenti, "Fast Visual Odometry and Mapping from RGB-D Data," In IEEE International Conference on Robotics and Automation (ICRA), pp.2305-2310, May, 2013.
  19. Ji Zhang, Michael Kaess, and Sanjiv Singh, "Realtime Depth Enhanced Monocular Odometry," in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September, 2014.
  20. Zheng Fang, Yu Zhang, "Experimental Evaluation of RGB-D Visual Odometry Methods," International Journal of Advanced Robotic Systems (IJARS), December, 2015.

Cited by

  1. 비정형의 건설환경 매핑을 위한 레이저 반사광 강도와 주변광을 활용한 향상된 라이다-관성 슬램 vol.16, pp.3, 2016, https://doi.org/10.7746/jkros.2021.16.3.179