Abstract
A visual simultaneous localization and mapping (SLAM) system usually contains a relocalization module to recover the camera pose after tracking failure. The core of this module is to establish correspondences between map points and key points in the image, which is typically achieved by local image feature matching. Since recently emerged binary features have orders of magnitudes higher extraction speed than traditional features such as scale invariant feature transform, they can be applied to develop a real-time relocalization module once an efficient method of binary feature matching is provided. In this paper, we propose such a method by indexing binary features with hashing. Being different from the popular locality sensitive hashing, the proposed method constructs the hash keys by an online learning process instead of pure randomness. Specifically, the hash keys are trained with the aim of attaining uniform hash buckets and high collision rates of matched feature pairs, which makes the method more efficient on approximate nearest neighbor search. By distributing the online learning into the simultaneous localization and mapping process, we successfully apply the method to SLAM relocalization. Experiments show that camera poses can be recovered in real time even when there are tens of thousands of landmarks in the map.
Similar content being viewed by others
References
Calonder, M., Lepetit, V., Strecha, C., Fua, P.: Brief: binary robust independent elementary features. In: Proceedings of European Conference Computer Vision, pp. 778–792 (2010)
Davison, A., Reid, I., Molton, N., Stasse, O.: Monoslam: real-time single camera slam. IEEE Trans. Pattern Anal. Mach. Intell. 29(6), 1052–1067 (2007)
Engel, J., Schöps, T., Cremers, D.: Lsd-slam: Large-scale direct monocular slam. In: Proceedings of European Conference on Computer Vision, pp. 834–849. Springer (2014)
Feng, Y., Fan, L., Wu, Y.: Online learning of binary feature indexing for real-time slam relocalization. In: Proceedings of Asian Conference on Computer Vision Workshops, pp. 206–217 (2014)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and auto cartography. Commun. ACM 24(6), 381–395 (1981)
Galvez-Lpez, D., Tardos, J.D.: Bags of binary words for fast place recognition in image sequences. IEEE Trans. Robot. 28(5), 1188–1197 (2012)
Gionis, A., Indyk, P., Motwani, R.: Similarity search in high dimensions via hashing. In: Proceedings of 25th International Conference on Very Large Data Bases, pp. 518–529 (1999)
Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: Proceedings of IEEE and ACM International Symposium on Mixed and Augmented Reality, pp. 225–234 (2007)
Lepetit, V., Moreno-Noguer, F., Fua, P.: Epnp: an accurate o(n) solution to the pnp problem. Int. J. Comput. Vis. 81(2), 155–166 (2009)
Leutenegger, S., Chli, M., Siegwart, R.: Brisk: binary robust invariant scalable keypoints. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2548–2555 (2011)
Lim, H., Sinha, S., Cohen, M., Uyttendaele, M.: Real-time image-based 6-dof localization in large-scale environments. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1043–1050 (2012)
Lowe, D.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)
Muja, M., Lowe, D.G.: Fast matching of binary features. In: Conference on Computer and Robot Vision, pp. 404–410 (2012)
Mur-Artal, R., Montiel, J.M.M., Tards, J.D.: Orb-slam: a versatile and accurate monocular slam system. IEEE Trans. Robot. 31(5), 1147–1163 (2015)
Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 2161–2168 (2006)
Ozuysal, M., Calonder, M., Lepetit, V., Fua, P.: Fast keypoint recognition using random ferns. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 448–461 (2010)
Rosten, E., Drummond, T.: Machine learning for high-speed corner detection. In: Proceedings of European Conference on Computer Vision, pp. 430–443 (2006)
Rublee, E., Rabaud, V., Konolige, K., Bradski, G.: Orb: an efficient alternative to sift or surf. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2564–2571 (2011)
Sattler, T., Leibe, B., Kobbelt, L.: Fast image-based localization using direct 2d-to-3d matching. In: Proceedings of IEEE International Conference on Computer Vision, pp. 667–674 (2011)
Silpa-Anan, C., Hartley, R.: Optimised kd-trees for fast image descriptor matching. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2008)
Straub, J., Hilsenbeck, S., Schroth, G., Huitl, R., Moller, A., Steinbach, E.: Fast relocalization for visual odometry using binary features. In: Proceedings of IEEE International Conference on Image Processing, pp. 2548–2552 (2013)
Tola, E., Lepetit, V., Fua, P.: Daisy: an efficient dense descriptor applied to wide-baseline stereo. IEEE Trans. Pattern Anal. Mach. Intell. 32(5), 815–830 (2010)
Trzcinski, T., Lepetit, V., Fua, P.: Thick boundaries in binary space and their influence on nearest-neighbor search. Pattern Recogn. Lett. 33(16), 2173–2180 (2012)
Williams, B., Klein, G., Reid, I.: Automatic relocalization and loop closing for real-time monocular slam. IEEE Trans. Pattern Anal. Mach. Intell. 33(9), 1699–1712 (2011)
Yianilos, P.: Data structures and algorithms for nearest neighbor search in general metric spaces. In: Proceedings of the fourth annual ACM-SIAM symposium on Discrete algorithms, pp. 311–321 (1993)
Yunpeng, L., Snavely, N., Huttenlocher, D., Fua, P.: Worldwide pose estimation using 3d point clouds. In: Proceedings of European Conference on Computer Vision, pp. 15–29 (2012)
Acknowledgements
This work was supported by the National High Technology Research and Development Program of China (2015AA020504), the National Natural Science Foundation of China under Grant No. 61572499, 61421004 and Nokia Research Grant No. LF14011659182.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Feng, Y., Wu, Y. & Fan, L. Real-time SLAM relocalization with online learning of binary feature indexing. Machine Vision and Applications 28, 953–963 (2017). https://doi.org/10.1007/s00138-017-0873-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00138-017-0873-z