Abstract
Many robotic applications that involve relocalization or 3D scene reconstruction, have a need of finding geometry between camera images captured from widely different viewpoints. Computing epipolar geometry between wide baseline image pairs is difficult because often there are many more outliers than inliers computed at the feature correspondence stage. Abundant outliers require the naive approach to compute a huge number of random solutions to give a suitable probability that the correct solution is found. Furthermore, large numbers of outliers can also cause false solutions to appear like true solutions. We present a new method called UNIQSAC for ng weights for features to guide the random solutions towards high quality features, helping find good solutions. We also present a new method to evaluate geometry solutions that is more likely to find correct solutions. We demonstrate in a variety of different outdoor environments using both monocular and stereo image-pairs that our method produces better estimates than existing robust estimation approaches.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Beis, J.S., Lowe, D.G.: Shape indexing using approximate nearest-neighbour search in high-dimensional spaces. In: 1997 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1997. Proceedings, pp. 1000–1006. IEEE (1997)
Chum, O., Matas, J.: Matching with prosac-progressive sample consensus. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, pp. 220–226. IEEE (2005)
Chum, O., Matas, J., Kittler, J.: Locally optimized ransac. In: Joint Pattern Recognition Symposium, pp. 236–243. Springer (2003)
Fragoso, V., Sen, P., Rodriguez, S., Turk, M.: Evsac: accelerating hypotheses generation by modeling matching scores with extreme value theory. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2472–2479 (2013)
Geiger, A., Ziegler, J., Stiller, C.: Stereoscan: Dense 3d reconstruction in real-time. In: Intelligent Vehicles Symposium (IV) (2011)
Goshen, L., Shimshoni, I.: Balanced exploration and exploitation model search for efficient epipolar geometry estimation. IEEE Trans. Pattern Anal. Mach. Intell. 30(7), 1230–1242 (2008)
Hartley, R., Zisserman, A.: Multiple View Geometry in Computer vision. Cambridge University Press (2003)
Hartmann, W., Havlena, M., Schindler, K.: Predicting matchability. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9–16 (2014)
Lee, J.A., Yow, K.C., Chia, A.Y.S.: Robust matching of building facades under large viewpoint changes. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 1258–1264. IEEE (2009)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Matas, J., Chum, O.: Randomized ransac with sequential probability ratio test. In: Tenth IEEE International Conference on Computer Vision, 2005. ICCV 2005, vol. 2, pp. 1727–1732. IEEE (2005)
Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide-baseline stereo from maximally stable extremal regions. Image Vis. Comput. 22(10), 761–767 (2004)
Sattler, T., Leibe, B., Kobbelt, L.: Scramsac: improving ransac’s efficiency with a spatial consistency filter. In: 2009 IEEE 12th International Conference on Computer Vision, pp. 2090–2097. IEEE (2009)
Sivic, J., Zisserman, A.: Video google: efficient visual search of videos. In: Toward Category-level Object Recognition, pp. 127–144. Springer (2006)
Talker, L., Moses, Y., Shimshoni, I.: Using spatial order to boost the elimination of incorrect feature matches. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1809–1817 (2016)
Tordoff, B., Murray, D.W.: Guided sampling and consensus for motion estimation. In: European Conference on Computer Vision, pp. 82–96. Springer (2002)
Acknowledgements
This work is supported under award numbers; USDA 2015-51181-24393, USDA 2016-67021-24535 and ARPA-e 1830-219-2020937.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Nuske, S., Patravali, J. (2018). Finding Better Wide Baseline Stereo Solutions Using Feature Quality. In: Hutter, M., Siegwart, R. (eds) Field and Service Robotics. Springer Proceedings in Advanced Robotics, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-319-67361-5_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-67361-5_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67360-8
Online ISBN: 978-3-319-67361-5
eBook Packages: EngineeringEngineering (R0)