Abstract
Although a wide variety of deep neural networks for robust Visual Odometry (VO) can be found in the literature, they are still unable to solve the drift problem in long-term robot navigation. Thus, this paper aims to propose novel deep end-to-end networks for long-term 6-DoF VO task. It mainly fuses relative and global networks based on Recurrent Convolutional Neural Networks (RCNNs) to improve the monocular localization accuracy. Indeed, the relative sub-networks are implemented to smooth the VO trajectory, while global sub-networks are designed to avoid drift problem. All the parameters are jointly optimized using Cross Transformation Constraints (CTC), which represents temporal geometric consistency of the consecutive frames, and Mean Square Error (MSE) between the predicted pose and ground truth. The experimental results on both indoor and outdoor datasets show that our method outperforms other state-of-the-art learning-based VO methods in terms of pose accuracy.
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
References
Cadena, C., et al.: Past, present, and future of simultaneous localization and mapping: toward the robust-perception age. IEEE Trans. Rob. 32(6), 1309–1332 (2016)
Özyeşil, O., Voroninski, V., Basri, R., Singer, A.: A survey of structure from motion. Acta Numerica 26, 305–364 (2017)
Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: ORB-SLAM: a versatile and accurate monocular slam system. IEEE Trans. Rob. 31(5), 1147–1163 (2015)
Klein, G., Murray, D.: Parallel tracking and mapping for small AR workspaces. In: 6th IEEE and ACM International Symposium on Mixed and Augmented Reality (ISMAR), pp. 225–234 (2007)
Engel, J., Schöps, T., Cremers, D.: LSD-SLAM: large-scale direct monocular SLAM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8690, pp. 834–849. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10605-2_54
Engel, J., Koltun, V., Cremers, D.: Direct sparse odometry. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 40(3), 611–625 (2018)
Li, R., Wang, S., Gu, D.: Ongoing evolution of visual SLAM from geometry to deep learning: challenges and opportunities. Cogn. Comput. 10(6), 875–889 (2018)
Clark, R., Wang, S., Markham, A., Trigoni, N., Wen, H.: VidLoc: a deep spatio-temporal model for 6-DoF video-clip relocalization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol. 3, pp. 2652–2660 (2017)
Valada, A., Radwan, N., Burgard, W.: Deep auxiliary learning for visual localization and odometry. arXiv preprint arXiv:1803.03642 (2018)
Kendall, A., Grimes, M., Cipolla, R.: PoseNet: a convolutional network for real-time 6-DoF camera relocalization. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 2938–2946 (2015)
Walch, F., Hazirbas, C., Leal-Taixe, L., Sattler, T., Hilsenbeck, S., Cremers, D.: Image-based localization using LSTMS for structured feature correlation. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 627–637 (2017)
Costante, G., Ciarfuglia, T.A.: LS-VO: learning dense optical subspace for robust visual odometry estimation. IEEE Robot. Autom. Lett. 3(3), 1735–1742 (2018)
Vijayanarasimhan, S., Ricco, S., Schmid, C., Sukthankar, R., Fragkiadaki, K.: SfM-Net: learning of structure and motion from video. arXiv preprint arXiv:1704.07804 (2017)
Iyer, G., Murthy, J.K., Gunshi Gupta, K., Paull, L.: Geometric consistency for self-supervised end-to-end visual odometry. arXiv preprint arXiv:1804.03789 (2018)
Brahmbhatt, S., Gu, J., Kim, K., Hays, J., Kautz, J.: Geometry-aware learning of maps for camera localization. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2616–2625 (2018)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2016)
Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs). arXiv preprint arXiv:1511.07289 (2015)
Wang, S., Clark, R., Wen, H., Trigoni, N.: End-to-end, sequence-to-sequence probabilistic visual odometry through deep neural networks. Int. J. Robot. Res. 37(4–5), 513–542 (2018)
Zaremba, W., Sutskever, I.: Learning to execute. arXiv preprint arXiv:1410.4615 (2014)
Shotton, J., Glocker, B., Zach, C., Izadi, S., Criminisi, A., Fitzgibbon, A.: Scene coordinate regression forests for camera relocalization in RGB-D images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2930–2937 (2013)
Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? The KITTI vision benchmark suite. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3354–3361 (2012)
Mohanty, V., Agrawal, S., Datta, S., Ghosh, A., Sharma, V.D., Chakravarty, D.: DeepVO: a deep learning approach for monocular visual odometry. arXiv preprint arXiv:1611.06069 (2016)
Zhao, C., Sun, L., Purkait, P., Duckett, T., Stolkin, R.: Learning monocular visual odometry with dense 3D mapping from dense 3D flow. arXiv preprint arXiv:1803.02286 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Lin, Y. et al. (2019). Deep Global-Relative Networks for End-to-End 6-DoF Visual Localization and Odometry. In: Nayak, A., Sharma, A. (eds) PRICAI 2019: Trends in Artificial Intelligence. PRICAI 2019. Lecture Notes in Computer Science(), vol 11671. Springer, Cham. https://doi.org/10.1007/978-3-030-29911-8_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-29911-8_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-29910-1
Online ISBN: 978-3-030-29911-8
eBook Packages: Computer ScienceComputer Science (R0)