This work is licensed under the Creative Commons Attribution 4.0 International License.
W. Chen et al., “An overview on visual SLAM: From tradition to semantic,” Remote Sens., vol. 14, no. 13, Jun. 2022, Art. no. 3010. https://doi.org/10.3390/rs14133010Search in Google Scholar
C. Cadena et al., “Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age,” IEEE Trans. Robot., vol. 32, no. 6, pp. 1309–1332, Dec. 2016. https://doi.org/10.1109/TRO.2016.2624754Search in Google Scholar
L. Lechelek, S. Horna, R. Zrour, M. Naudin, and C. Guillevin, “A hybrid method for 3D reconstruction of MR images,” Journal of Imaging, vol. 8, no. 4, 2022, Art. no. 103. https://doi.org/10.3390/jimaging8040103Search in Google Scholar
K. Hu, J. Wu, Y. Li, M. Lu, L. Weng, and M. Xia, “FedGCN: Federated learning-based graph convolutional networks for non-Euclidean spatial data,” Mathematics, vol. 10, no. 6, 2022, Art. no. 1000. https://doi.org/10.3390/math10061000Search in Google Scholar
K. Hu, C. Weng, Y. Zhang, J. Jin, and Q. Xia, “An overview of underwater vision enhancement: From traditional methods to recent deep learning,” J. Mar. Sci. Eng., vol. 10, no. 2, Feb. 2022, Art. no. 241. https://doi.org/10.3390/jmse10020241Search in Google Scholar
K. Hu, M. Li, M. Xia, and H. Lin, “Multi-scale feature aggregation network for water area segmentation,” Remote Sensing, vol. 14, no. 1, Jan. 2022, Art. no. 206. https://doi.org/10.3390/rs14010206Search in Google Scholar
H. M. S. Bruno and E. L. Colombini, “LIFT-SLAM: A deep-learning feature-based monocular visual SLAM method,” Neurocomputing, vol. 455, pp. 97–110, Sep. 2021. https://doi.org/10.1016/j.neucom.2021.05.027Search in Google Scholar
Y. Cao, Y. Luo, and T. Wang, “ORB-SLAM implementation using deep learning methods for visual odometry.” [Online]. Available: https://ty-wang.github.io/data/slam_report.pdfSearch in Google Scholar
X. Gao and T. Zhang, “Unsupervised learning to detect loops using deep neural networks for visual SLAM system,” Auton. Robots, vol. 41, no. 1, pp. 1–18, Dec. 2017. https://doi.org/10.1007/s10514-015-9516-2Search in Google Scholar
J. Oh and G. Eoh, “Variational Bayesian approach to condition-invariant feature extraction for visual place recognition,” Applied Sciences, vol. 11, no. 19, Sep. 2021, Art. no. 8976. https://doi.org/10.3390/app11198976Search in Google Scholar
R. Mur-Artal and J. D. Tardos, “ORB-SLAM2 : an open-source SLAM system for monocular, stereo and RGB-D cameras,” IEEE Trans. Robot., vol. 33, no. 5, pp. 1255–1262, Oct. 2017. https://doi.org/10.1109/TRO.2017.2705103Search in Google Scholar
C. Campos, R. Elvira, J. J. G. Rodriguez, J. M. M. Montiel, and J. D. Tardos, “ORB-SLAM3: An accurate open-source library for visual, visual-inertial, and multimap SLAM,” IEEE Trans. Robot., vol. 37, no. 6, pp. 1874–1890, May 2021. https://doi.org/10.1109/TRO.2021.3075644Search in Google Scholar
A. Steenbeek and F. Nex, “CNN-based dense monocular visual SLAM for real-time UAV exploration in emergency conditions,” Drones, vol. 6, no. 3, Mar. 2022, Art. no. 79. https://doi.org/10.3390/drones6030079Search in Google Scholar
K. Tateno, F. Tombari, I. Laina, and N. Navab, “CNN-SLAM: Real-time dense monocular SLAM with learned depth prediction,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, Jul. 2017, pp. 6565–6574. https://doi.org/10.1109/CVPR.2017.695Search in Google Scholar
M. Bloesch, J. Czarnowski, R. Clark, S. Leutenegger, and A. J. Davison, “CodeSLAM – Learning a compact, optimisable representation for dense visual SLAM,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Salt Lake City, UT, USA, Jun. 2018, pp. 2560–2568. https://doi.org/10.1109/CVPR.2018.00271Search in Google Scholar
N. Yang, R. Wang, J. Stückler, and D. Cremers, “Deep virtual stereo odometry: Leveraging deep depth prediction for monocular direct sparse odometry,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss, Eds., vol. 11212. Springer, Cham, 2018, pp. 835–852. https://doi.org/10.1007/978-3-030-01237-3_50Search in Google Scholar
D. Bojanic, K. Bartol, T. Pribanic, T. Petkovic, Y. D. Donoso, and J. S. Mas, “On the comparison of classic and deep keypoint detector and descriptor methods,” in Int. Symp. Image Signal Process. Anal. ISPA, vol. 2019, Dubrovnik, Croatia, Sep. 2019, pp. 64–69. https://doi.org/10.1109/ISPA.2019.8868792Search in Google Scholar
S. Dara and P. Tumma, “Feature extraction by using Deep Learning: A survey,” in Proc. 2nd Int. Conf. Electron. Commun. Aerosp. Technol. ICECA 2018, Coimbatore, India, Mar. 2018, pp. 1795–1801. https://doi.org/10.1109/ICECA.2018.8474912Search in Google Scholar
C. Forster, Z. Zhang, M. Gassner, M. Werlberger, and D. Scaramuzza, “SVO : Semi-direct visual odometry for monocular and multi-camera systems,” in 2014 IEEE Int. Conf. Robot. Autom., 2014, pp. 1–18. [Online]. Available: https://rpg.ifi.uzh.ch/docs/TRO16_Forster-SVO.pdfSearch in Google Scholar
J. Engel, V. Koltun, and D. Cremers, “Direct Sparse Odometry,” 2016. [Online]. Available: https://jakobengel.github.io/pdf/DSO.pdfSearch in Google Scholar
D. G. Lowe, “Object recognition from local scale-invariant features,” in Proceedings of the Seventh IEEE International Conference on Computer Vision, vol. 2, Kerkyra, Greece, Sep. 1999, pp. 1150–1157. https://doi.org/10.1109/ICCV.1999.790410Search in Google Scholar
H. Bay, T. Tuytelaars, and L. Van Gool, “LNCS 3951 – SURF: Speeded up robust features,” in Computer Vision – ECCV 2006. Lecture Notes in Computer Science, A. Leonardis, H. Bischof, and A. Pinz, Eds., vol 3951. Springer, Berlin, Heidelberg., 2006, pp. 404–417. https://doi.org/10.1007/11744023_32Search in Google Scholar
M. Calonder, V. Lepetit, M. Özuysal, T. Trzcinski, C. Strecha, and P. Fua, “BRIEF: Computing a local binary descriptor very fast,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 34, no. 7, pp. 1281–1298, 2012. https://doi.org/10.1109/TPAMI.2011.222Search in Google Scholar
E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “ORB: An efficient alternative to SIFT or SURF,” in Proc. IEEE Int. Conf. Comput. Vis., Barcelona, Spain, Nov. 2011, pp. 2564–2571. https://doi.org/10.1109/ICCV.2011.6126544Search in Google Scholar
D. Detone, T. Malisiewicz, and A. Rabinovich, “SuperPoint: Self-supervised interest point detection and description,” in IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work., Salt Lake City, UT, USA, Jun. 2018, pp. 337–349. https://doi.org/10.1109/CVPRW.2018.00060Search in Google Scholar
K. M. Yi, E. Trulls, V. Lepetit, and P. Fua, “LIFT: Learned invariant feature transform,” in Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds., vol. 9910 LNCS. Springer, Cham, 2016, pp. 467–483. https://doi.org/10.1007/978-3-319-46466-4_28Search in Google Scholar
C. B. Choy, J. Y. Gwak, S. Savarese, and M. Chandraker, “Universal correspondence network,” Adv. Neural Inf. Process. Syst., pp. 2414–2422, Jun. 2016.Search in Google Scholar
E. Simo-Serra, E. Trulls, L. Ferraz, I. Kokkinos, P. Fua, and F. Moreno-Noguer, “Discriminative learning of deep convolutional feature point descriptors,” in 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, Dec. 2015, pp. 118–126. https://doi.org/10.1109/ICCV.2015.22Search in Google Scholar
C. Deng, K. Qiu, R. Xiong, and C. Zhou, “Comparative study of Deep Learning based features in SLAM,” in 2019 4th Asia-Pacific Conference on Intelligent Robot Systems (ACIRS), Nagoya, Japan, Jul. 2019, pp. 250–254. https://doi.org/10.1109/ACIRS.2019.8935995Search in Google Scholar
X. Han, Y. Tao, Z. Li, R. Cen, and F. Xue, “SuperPointVO: A lightweight visual odometry based on CNN feature extraction,” in 2020 5th International Conference on Automation, Control and Robotics Engineering (CACRE), Dalian, China, Sep. 2020, pp. 685–691. https://doi.org/10.1109/CACRE50138.2020.9230348Search in Google Scholar
D. DeTone, T. Malisiewicz, and A. Rabinovich, “Self-improving visual odometry,” CoRR, vol.abs/1812.03245, 2018. [Online]. Available: http://arxiv.org/abs/1812.03245Search in Google Scholar
N. Yang, L. Von Stumberg, R. Wang, and D. Cremers, “D3VO: Deep depth, deep pose and deep uncertainty for monocular visual odometry,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Seattle, WA, USA, Jun. 2020, pp. 1278–1289. https://doi.org/10.1109/CVPR42600.2020.00136Search in Google Scholar
H. Zhan, C. S. Weerasekera, J. W. Bian, and I. Reid, “Visual odometry revisited: What should be learnt?,” in Proc. – IEEE Int. Conf. Robot. Autom., Paris, France, May 2020, pp. 4203–4210. https://doi.org/10.1109/ICRA40945.2020.9197374Search in Google Scholar
A. Ranjan et al., “Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Long Beach, CA, USA, Jun. 2019, pp. 12232–12241. https://doi.org/10.1109/CVPR.2019.01252Search in Google Scholar
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., Las Vegas, NV, USA, Dec. 2016, pp. 770–778. https://doi.org/10.1109/CVPR.2016.90Search in Google Scholar
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in 3rd Int. Conf. Learn. Represent. ICLR 2015 – Conf. Track Proc., 2015, pp. 1–14. https://doi.org/10.48550/arXiv.1409.1556Search in Google Scholar
J. Hu, M. Ozay, Y. Zhang, and T. Okatani, “Revisiting single image depth estimation: Toward higher resolution maps with accurate object boundaries,” Proc. – 2019 IEEE Winter Conf. Appl. Comput. Vision, WACV 2019, Waikoloa, HI, USA, Jan. 2019, pp. 1043–1051. https://doi.org/10.1109/WACV.2019.00116Search in Google Scholar
I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab, “Deeper depth prediction with fully convolutional residual networks,” in Proc. – 2016 4th Int. Conf. 3D Vision, 3DV 2016, Stanford, CA, USA, Oct. 2016, pp. 239–248. https://doi.org/10.1109/3DV.2016.32Search in Google Scholar
F. Mal and S. Karaman, “Sparse-to-dense: Depth prediction from sparse depth samples and a single image,” in Proc. – IEEE Int. Conf. Robot. Autom., Brisbane, QLD, Australia, May 2018, pp. 4796–4803. https://doi.org/10.1109/ICRA.2018.8460184Search in Google Scholar
Y. Y. Jau, R. Zhu, H. Su, and M. Chandraker, “Deep keypoint-based camera pose estimation with geometric constraints,” in IEEE Int. Conf. Intell. Robot. Syst., Las Vegas, NV, USA, Oct. 2020, pp. 4950–4957. https://doi.org/10.1109/IROS45743.2020.9341229Search in Google Scholar
T. Y. Lin et al., “Microsoft COCO: Common objects in context,” in Computer Vision – ECCV 2014. Lecture Notes in Computer Science, D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars, Eds., vol 8693. Springer, Cham., 2014, pp. 740–755. https://doi.org/10.1007/978-3-319-10602-1_48Search in Google Scholar
P. K. N. Silberman, D. Hoiem and R. Fergus, “Indoor segmentation and support inference from RGBD images,” in Computer Vision – ECCV 2012. Lecture Notes in Computer Science, A. Fitzgibbon, S. Lazebnik, P. Perona, Y. Sato, and C. Schmid, Eds., vol 7576. Springer, Berlin, Heidelberg, 2012, pp. 746–760. https://doi.org/10.1007/978-3-642-33715-4_54Search in Google Scholar
A. Dancu, M. Fourgeaud, Z. Franjcic, and R. Avetisyan, “Underwater reconstruction using depth sensors,” in SA’14, SIGGRAPH Asia 2014 Tech. Briefs, Nov. 2014, Art. no. 2, pp. 1–4. https://doi.org/10.1145/2669024.2669042Search in Google Scholar
S. T. Digumarti, G. Chaurasia, A. Taneja, R. Siegwart, A. Thomas, and P. Beardsley, “Underwater 3D capture using a low-cost commercial depth camera,” in 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), Lake Placid, NY, USA, 2016, pp. 1–9. https://doi.org/10.1109/WACV.2016.7477644Search in Google Scholar
N. Wang, Y. Zhou, F. Han, H. Zhu, and Y. Zheng, “UWGAN: Underwater GAN for real-world underwater color restoration and dehazing,” arXiv 2019, arXiv:1912.10269, pp. 1–10, 2019. https://arxiv.org/ftp/arxiv/papers/1912/1912.10269.pdfSearch in Google Scholar
M. J. Islam, Y. Xia, and J. Sattar, “Fast underwater image enhancement for improved visual perception,” IEEE Robot. Autom. Lett., vol. 5, no. 2, pp. 3227–3234, Feb. 2020. https://doi.org/10.1109/LRA.2020.2974710Search in Google Scholar
C. Fabbri, M. J. Islam, and J. Sattar, “Enhancing underwater imagery using generative adversarial networks,” in Proc. - IEEE Int. Conf. Robot. Autom., Brisbane, QLD, Australia, May 2018, pp. 7159–7165. https://doi.org/10.1109/ICRA.2018.8460552Search in Google Scholar
M. Trajković and M. Hedley, “Fast corner detection,” Image Vis. Comput., vol. 16, no. 2, pp. 75–87, Feb. 1998. https://doi.org/10.1016/S0262-8856(97)00056-5Search in Google Scholar
V. Balntas, K. Lenc, A. Vedaldi, and K. Mikolajczyk, “HPatches: A benchmark and evaluation of handcrafted and learned local descriptors,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, Jul. 2017, pp. 3852–3861. https://doi.org/10.1109/CVPR.2017.410Search in Google Scholar
A. Duarte, F. Codevilla, J. D. O. Gaya, and S. S. C. Botelho, “A dataset to evaluate underwater image restoration methods,” in OCEANS 2016 – Shanghai, Shanghai, China, Apr. 2016, pp. 1–6. https://doi.org/10.1109/OCEANSAP.2016.7485524Search in Google Scholar
C. Li et al., “An underwater image enhancement benchmark dataset and beyond,” IEEE Trans. Image Process., vol. 29, pp. 4376–4389, Nov. 2020. https://doi.org/10.1109/TIP.2019.2955241Search in Google Scholar
M. Ferrera, V. Creuze, J. Moras, and P. Trouvé-Peloux, “AQUALOC: An underwater dataset for visual-inertial-pressure localization,” Int. J. Rob. Res., vol. 38, no. 14, pp. 1549–1559, Oct. 2019. https://doi.org/10.1177/0278364919883346Search in Google Scholar
J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, “A benchmark for the evaluation of RGB-D SLAM systems,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, Vilamoura-Algarve, Portugal, Oct. 2012, pp. 573–580. https://doi.org/10.1109/IROS.2012.6385773Search in Google Scholar