Egomotion estimation using assorted features

International Journal of Computer Vision

Volumn 98, Issue 2, 2012, Pages 202-216

  Pradeep, Vivek ^a   Lim, Jongwoo ^b  

^a MICROSOFT   (United States)

^b HONDA RESEARCH INSTITUTE   (United States)

Author keywords

SLAM; Structure from motion; Tracking; Visual odometry

Indexed keywords

CAMERA MOTION PARAMETERS; CAMERA MOTIONS; EGO-MOTION ESTIMATION; LINE FEATURES; MATHEMATICAL FRAMEWORKS; POINT FEATURES; POLYNOMIAL SYSTEMS; QUATERNION REPRESENTATION; ROTATION MATRICES; SLAM; STEREO RIGS; STRUCTURE FROM MOTION; TRIFOCAL TENSOR; VISUAL ODOMETRY;

ALGORITHMS; INFORMATION SCIENCE; POLYNOMIALS; SURFACE DISCHARGES; VISION;

COMPUTER VISION;

EID: 84868302778     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-011-0504-5     Document Type: Article

References (35)

1. Ansar, A., & Daniilidis, K. (2003). Linear pose estimation from points or lines. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(5), 578-589.
2. Bartoli, A., & Sturm, P. (2003). Multiple-view structure and motion from line correspondences. In ICCV.
3. Bujnak,M., Kukelova, Z.,&Pajdla, T. (2008). A general solution to the p4p problem for camera with unknown focal length. In IEEE computer society conference on computer vision and pattern recognition (CVPR) (pp. 1-8).
4. Chandraker, M., Lim, J., & Kreigman, D. J. (2009). Moving in stereo: efficient structure and motion using lines. In ICCV.
5. Christy, S., & Horaud, R. (1999). Iterative pose computation from line correspondences. Computer Vision and Image Understanding, 73(1), 137-144.
6. Comport, A.,Malis, E., & Rives, P. (2007). Accurate quadrifocal tracking for robust 3d visual odometry. In ICRA (pp. 40-45). (Pubitemid 350140162)
7. Dornaika, F., & Garcia, C. (1999). Pose estimation using point and line correspondences. Real-Time Imaging, 5(3), 215-230.
8. Fischler, M. A., & Bolles, R. C. (1997). Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. International Journal of Computer Vision, 22(2), 125-140.
9. Haralick, R., Lee, C., Ottenberg, K., & Nolle, M. (1991). Analysis and solutions of the three point perspective pose estimation problem. In IEEE computer society conference on computer vision and pattern recognition (CVPR).
10. Hartley, R. (1997). Lines and points in three views and the trifocal tensor. International Journal of Computer Vision, 22, 125-140. (Pubitemid 127507393)
11. Hartley, R. (1998). Computation of the trifocal tensor. In ECCV (pp. 20-35).
12. Hartley, R. I., & Zisserman, A. (2000). Multiple view geometry in computer vision. Cambridge: Cambridge University Press, ISBN:0521623049.
13. Heyden, A. (1995). Geometry and algebra of multiple projective transformations. PhD thesis, Lund University.
14. Horn, B. K. P. (1987). Closed-form solution of absolute orientation using unit quaternions. Journal of the Optical Society of America, 4, 629-642.
15. Kukelova, Z., Bujnak, M., & Pajdla, T. (2008). Automatic generator of minimal problem solvers. In ECCV (pp. 302-315).
16. Kukelova, Z., Bujnak, M., & Pajdla, T. (2008). Polynomial eigenvalue solutions to the 5-pt and 6-pt relative pose problems. In BMVC.
17. Li, H., & Hartley, R. (2006). Five-point motion estimation made easy. In ICPR (Vol. 2)
18. Liu, Y., & Huang, T. (1988). A linear algorithm for motion estimation using straight line correspondences. In ICPR (pp. 213-219).
19. Lowe, D. (1999). Object recognition from local scale-invariant features. In Proceedings of the international conference on computer vision (pp. 1150-1157). (Pubitemid 32079997)
20. Lucas, B. D.,&Kanade, T. (1981). An iterative image registration technique with an application to stereo vision. In International joint conferences on oratorical intelligence (IJCAI) (pp. 1151-1156).
21. Neira, J., Tardos, J. D., Horn, J., & Schmidt, G. (1999). Fusing range and intensity images for mobile robot localization. IEEE Transactions on Robotics and Automation, 15, 76-84.
22. Nister, D. (2004). An efficient solution to the five-point relative pose problem. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(6), 756-770.
23. Nister, D., Naroditsky, O., & Bergen, J. (2004). Visual odometry. In IEEE computer society conference on computer vision and pattern recognition (CVPR) (Vol. 1, pp. 652-659).
24. Oliensis, J.,&Werman, M. (2000). Structure frommotion using points, lines, and intensities. In IEEE computer society conference on computer vision and pattern recognition (CVPR) (Vol. 2).
25. Pollefeys, M., Nister, D., & et al. (2007). Detailed real-time urban 3d reconstruction from video. In IJCV.
26. Pradeep, V., & Lim, J. (2010). Egomotion using assorted features. In IEEE computer society conference on computer vision and pattern recognition (CVPR) (pp. 1514-1521).
27. Rosten, E., & Drummond, T. (2005). Fusing points and lines for high performance tracking. In ICCV (Vol. 2, pp. 1508-1515). (Pubitemid 44042356)
28. Seitz, S., & Anandan, P. (1999). Implicit representation and scene reconstruction from probability density functions. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR) (Vol. 2).
29. Shashua, A., & Wolf, L. (2000). On the structure and properties of the quadrifocal tensor. In Lecture notes in computer science (pp. 710-724).
30. Stewénius, H., Engels, C., & Nister, D. (2006). Recent developments on direct relative orientation. Journal of Photogrammetry and Remote Sensing, 60, 284-294. (Pubitemid 43815728)
31. Torr, P. H. S., & Zisserman, A. (1997). Robust parameterization and computation of the trifocal tensor. Image and Vision Computing, 15, 591-605. (Pubitemid 127381936)
32. Triggs, B. (1999). Camera pose and calibration from 4 or 5 known 3d points. In ICCV (pp. 278-284). (Pubitemid 32080051)
33. von Gioi, R. G., Jakubowicz, J., Morel, J.-M., & Randall, G. (2010). Lsd: a fast line segment detector with a false detection control. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 722-732.
34. Zhang, Z. (1998). Determining the epipolar geometry and its uncertainty: a review. International Journal of Computer Vision, 27, 161-195. (Pubitemid 128529716)
35. Zhu, Z., Oskiper, T., Samarasekera, S., Kumar, R., & Sawhney, H. S. (2007). Ten-fold improvement in visual odometry using landmark matching. In International conference on computer vision (ICCV) (pp. 1-8).