More Web Proxy on the site http://driver.im/

article

Egomotion Estimation Using Assorted Features

Authors:

Jongwoo LimAuthors Info & Claims

International Journal of Computer Vision, Volume 98, Issue 2

Pages 202 - 216

https://doi.org/10.1007/s11263-011-0504-5

Published: 01 June 2012 Publication History

Abstract

We propose a novel minimal solver for recovering camera motion across two views of a calibrated stereo rig. The algorithm can handle any assorted combination of point and line features across the four images and facilitates a visual odometry pipeline that is enhanced by well-localized and reliably-tracked line features while retaining the well-known advantages of point features. The mathematical framework of our method is based on trifocal tensor geometry and a quaternion representation of rotation matrices. A simple polynomial system is developed from which camera motion parameters may be extracted more robustly in the presence of severe noise, as compared to the conventionally employed direct linear/subspace solutions. This is demonstrated with extensive experiments and comparisons against the 3-point and line-sfm algorithms.

References

[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.

Digital Library

[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.

Digital Library

[6]

Comport, A., Malis, E., & Rives, P. (2007). Accurate quadrifocal tracking for robust 3d visual odometry. In ICRA (pp. 40-45).

[7]

Dornaika, F., & Garcia, C. (1999). Pose estimation using point and line correspondences. Real-Time Imaging, 5(3), 215-230.

Digital Library

[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.

[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).

[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.

Digital Library

[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).

[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.

[31]

Torr, P. H. S., & Zisserman, A. (1997). Robust parameterization and computation of the trifocal tensor. Image and Vision Computing, 15, 591-605.

[32]

Triggs, B. (1999). Camera pose and calibration from 4 or 5 known 3d points. In ICCV (pp. 278-284).

[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.

Digital Library

[34]

Zhang, Z. (1998). Determining the epipolar geometry and its uncertainty: a review. International Journal of Computer Vision, 27, 161-195.

Digital Library

[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).

Cited By

Yuan ZCheng JYang X(2023)CR-LDSO: Direct Sparse LiDAR-Assisted Visual Odometry With Cloud ReusingIEEE Transactions on Multimedia10.1109/TMM.2023.325216125(9397-9409)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2023.3252161
Yasuda YMartins LCappabianco F(2020)Autonomous Visual Navigation for Mobile RobotsACM Computing Surveys10.1145/336896153:1(1-34)Online publication date: 6-Feb-2020
https://dl.acm.org/doi/10.1145/3368961
Vakhitov ALempitsky VZheng Y(2018)Stereo Relative Pose from Line and Point Feature TripletsComputer Vision – ECCV 201810.1007/978-3-030-01237-3_40(662-677)Online publication date: 8-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-01237-3_40
Show More Cited By

Egomotion Estimation Using Assorted Features
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision tasks
  2. Computer graphics
    1. Animation

Recommendations

Semi-dense Visual Odometry for a Monocular Camera
ICCV '13: Proceedings of the 2013 IEEE International Conference on Computer Vision

We propose a fundamentally novel approach to real-time visual odometry for a monocular camera. It allows to benefit from the simplicity and accuracy of dense tracking - which does not depend on visual features - while running in real-time on a CPU. The ...
Predictive monocular odometry (PMO)

Visual odometry using only a monocular camera faces more algorithmic challenges than stereo odometry. We present a robust monocular visual odometry framework for automotive applications. An extended propagation-based tracking framework is proposed which ...
Probabilistic Egomotion for Stereo Visual Odometry

We present a novel approach of Stereo Visual Odometry for vehicles equipped with calibrated stereo cameras. We combine a dense probabilistic 5D egomotion estimation method with a sparse keypoint based stereo approach to provide high quality estimates of ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image International Journal of Computer Vision

International Journal of Computer Vision Volume 98, Issue 2

June 2012

119 pages

ISSN:0920-5691

Issue’s Table of Contents

Copyright © Copyright © 2012 Springer Science+Business Media, LLC.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 June 2012

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Yuan ZCheng JYang X(2023)CR-LDSO: Direct Sparse LiDAR-Assisted Visual Odometry With Cloud ReusingIEEE Transactions on Multimedia10.1109/TMM.2023.325216125(9397-9409)Online publication date: 1-Jan-2023
https://dl.acm.org/doi/10.1109/TMM.2023.3252161
Yasuda YMartins LCappabianco F(2020)Autonomous Visual Navigation for Mobile RobotsACM Computing Surveys10.1145/336896153:1(1-34)Online publication date: 6-Feb-2020
https://dl.acm.org/doi/10.1145/3368961
Vakhitov ALempitsky VZheng Y(2018)Stereo Relative Pose from Line and Point Feature TripletsComputer Vision – ECCV 201810.1007/978-3-030-01237-3_40(662-677)Online publication date: 8-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-030-01237-3_40
Zuo XXie XLiu YHuang G(2017)Robust visual SLAM with point and line features2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)10.1109/IROS.2017.8205991(1775-1782)Online publication date: 24-Sep-2017
https://dl.acm.org/doi/10.1109/IROS.2017.8205991
Zhang GLee JLim JSuh I(2015)Building a 3-D Line-Based Map Using Stereo SLAMIEEE Transactions on Robotics10.1109/TRO.2015.248949831:6(1364-1377)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1109/TRO.2015.2489498
Fathi HDai FLourakis M(2015)Automated as-built 3D reconstruction of civil infrastructure using computer visionAdvanced Engineering Informatics10.1016/j.aei.2015.01.01229:2(149-161)Online publication date: 1-Apr-2015
https://dl.acm.org/doi/10.1016/j.aei.2015.01.012
Fathi HBrilakis I(2013)A videogrammetric as-built data collection method for digital fabrication of sheet metal roof panelsAdvanced Engineering Informatics10.1016/j.aei.2013.04.00627:4(466-476)Online publication date: 1-Oct-2013
https://dl.acm.org/doi/10.1016/j.aei.2013.04.006

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents