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Simultaneous Localization and Mapping (SLAM) stands as one of the critical challenges in robot navigation.
M. Montemerlo, S. Thrun, D. Koller, B. Wegbreit, et al. , “Fastslam: A factored solution to the simultaneous localization and mapping problem,” Aaai/iaai , vol. 593598, 2002
2002
Earlier work this paper cites.
A. J. Davison, I. D. Reid, N. D. Molton, and O. Stasse, “Monoslam: Real-time single camera slam,” IEEE transactions on pattern analysis and machine intelligence , vol. 29, no. 6, pp. 1052–1067, 2007
2007
Earlier work this paper cites.
N. Sünderhauf and P. Protzel, “Towards a robust back-end for pose graph slam,” in 2012 IEEE international conference on robotics and automation . IEEE, 2012, pp. 1254–1261
2012
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1231–1237, 2013
2013
Earlier work this paper cites.
M. Labbe and F. Michaud, “Online global loop closure detection for large-scale multi-session graph-based slam,” in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2014, pp. 2661–2666
2014
Earlier work this paper cites.
J. Engel, T. Schöps, and D. Cremers, “Lsd-slam: Large-scale direct monocular slam,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part II 13 . Springer, 2014, pp. 834–849
2014
Earlier work this paper cites.
K. Yousif, A. Bab-Hadiashar, and R. Hoseinnezhad, “An overview to visual odometry and visual slam: Applications to mobile robotics,” Intelligent Industrial Systems , vol. 1, no. 4, pp. 289–311, 2015
2015
Earlier work this paper cites.
S. Leutenegger, S. Lynen, M. Bosse, R. Siegwart, and P. Furgale, “Keyframe-based visual–inertial odometry using nonlinear optimization,” The International Journal of Robotics Research , vol. 34, no. 3, pp. 314–334, 2015
2015
Earlier work this paper cites.
C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, J. Neira, I. Reid, and J. J. Leonard, “Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age,” IEEE Transactions on robotics , vol. 32, no. 6, pp. 1309–1332, 2016
2016
Earlier work this paper cites.
M. Burri, J. Nikolic, P. Gohl, T. Schneider, J. Rehder, S. Omari, M. W. Achtelik, and R. Siegwart, “The euroc micro aerial vehicle datasets,” The International Journal of Robotics Research , vol. 35, no. 10, pp. 1157–1163, 2016
2016
Earlier work this paper cites.
R. Mur-Artal and J. D. Tardós, “Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras,” IEEE transactions on robotics , vol. 33, no. 5, pp. 1255–1262, 2017
2017
Earlier work this paper cites.
J. Engel, V. Koltun, and D. Cremers, “Direct sparse odometry,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 3, pp. 611–625, 2017
2017
Earlier work this paper cites.
T. Qin, P. Li, and S. Shen, “Vins-mono: A robust and versatile monocular visual-inertial state estimator,” IEEE Transactions on Robotics , vol. 34, no. 4, pp. 1004–1020, 2018
2018
Earlier work this paper cites.
R. Li, S. Wang, Z. Long, and D. Gu, “Undeepvo: Monocular visual odometry through unsupervised deep learning,” in 2018 IEEE international conference on robotics and automation (ICRA) . IEEE, 2018, pp. 7286–7291
2018
Cited alongside, same era.
S. Khamis, S. Fanello, C. Rhemann, A. Kowdle, J. Valentin, and S. Izadi, “Stereonet: Guided hierarchical refinement for real-time edge-aware depth prediction,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 573–590
2018
Cited alongside, same era.
C. Tang and P. Tan, “Ba-net: Dense bundle adjustment network,” International Conference on Learning Representations (ICLR) , 2019
2019
Cited alongside, same era.
L. Han, Y. Lin, G. Du, and S. Lian, “Deepvio: Self-supervised deep learning of monocular visual inertial odometry using 3d geometric constraints,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 6906–6913
2019
Cited alongside, same era.
R. Liu, J. Gao, J. Zhang, D. Meng, and Z. Lin, “Investigating bi-level optimization for learning and vision from a unified perspective: A survey and beyond,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 44, no. 12, pp. 10 045–10 067, 2021
2021
Later among the works it cites.
K. Ji, J. Yang, and Y. Liang, “Bilevel optimization: Convergence analysis and enhanced design,” in International conference on machine learning . PMLR, 2021, pp. 4882–4892
2021
Later among the works it cites.
P. Wei, G. Hua, W. Huang, F. Meng, and H. Liu, “Unsupervised monocular visual-inertial odometry network,” in Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence , 2021, pp. 2347–2354
2021
Later among the works it cites.
R. Wang, Z. Hua, G. Liu, J. Zhang, J. Yan, F. Qi, S. Yang, J. Zhou, and X. Yang, “A bi-level framework for learning to solve combinatorial optimization on graphs,” Advances in Neural Information Processing Systems , vol. 34, pp. 21 453–21 466, 2021
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S. Zhao, P. Wang, H. Zhang, Z. Fang, and S. Scherer, “Tp-tio: A robust thermal-inertial odometry with deep thermalpoint,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 4505–4512
2020
Cited alongside, same era.
W. Wang, D. Zhu, X. Wang, Y. Hu, Y. Qiu, C. Wang, Y. Hu, A. Kapoor, and S. Scherer, “Tartanair: A dataset to push the limits of visual slam,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 4909–4916
2020
Cited alongside, same era.
J. Czarnowski, T. Laidlow, R. Clark, and A. J. Davison, “Deepfactors: Real-time probabilistic dense monocular slam,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 721–728, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Z. Teed and J. Deng, “Deepv2d: Video to depth with differentiable structure from motion,” International Conference on Learning Representations (ICLR) , 2020
2020
Cited alongside, same era.
W. Wang, Y. Hu, and S. Scherer, “Tartanvo: A generalizable learning-based vo,” in Conference on Robot Learning . PMLR, 2021, pp. 1761–1772
2021
Cited alongside, same era.
Z. Teed and J. Deng, “Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras,” Advances in neural information processing systems , vol. 34, pp. 16 558–16 569, 2021
2021
Cited alongside, same era.
C. Campos, R. Elvira, J. J. G. Rodríguez, J. M. Montiel, and J. D. Tardós, “Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam,” IEEE Transactions on Robotics , vol. 37, no. 6, pp. 1874–1890, 2021
2021
Cited alongside, same era.
2021
Later among the works it cites.
D. Gao, C. Wang, and S. Scherer, “Airloop: Lifelong loop closure detection,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 10 664–10 671
2022
Later among the works it cites.
C. M. Parameshwara, G. Hari, C. Fermüller, N. J. Sanket, and Y. Aloimonos, “Diffposenet: Direct differentiable camera pose estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2022, pp. 6845–6854
2022
Later among the works it cites.
M. Yang, Y. Chen, and H.-S. Kim, “Efficient deep visual and inertial odometry with adaptive visual modality selection,” in Computer Vision–ECCV 2022: 17th European Conference, Tel Aviv, Israel, October 23–27, 2022, Proceedings, Part XXXVIII . Springer, 2022, pp. 233–250
2022
Later among the works it cites.
K. Xu, Y. Hao, S. Yuan, C. Wang, and L. Xie, “Airvo: An illumination-robust point-line visual odometry,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 3429–3436
2023
Closest in time.
C. Wang, D. Gao, K. Xu, J. Geng, Y. Hu, Y. Qiu, B. Li, F. Yang, B. Moon, A. Pandey, Aryan, J. Xu, T. Wu, H. He, D. Huang, Z. Ren, S. Zhao, T. Fu, P. Reddy, X. Lin, W. Wang, J. Shi, R. Talak, K. Cao, Y. Du, H. Wang, H. Yu, S. Wang, S. Chen, A. Kashyap, R. Bandaru, K. Dantu, J. Wu, L. Xie, L. Carlone, M. Hutter, and S. Scherer, “PyPose: A library for robot learning with physics-based optimization,” in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2023
2023
Closest in time.
F. Yang, C. Wang, C. Cadena, and M. Hutter, “iplanner: Imperative path planning,” in Robotics: Science and Systems (RSS) , 2023
2023
Closest in time.
2023
Closest in time.
S. Wang, R. Clark, H. Wen, and N. Trigoni, “Deepvo: Towards end-to-end visual odometry with deep recurrent convolutional neural networks,” in 2017 IEEE international conference on robotics and automation (ICRA) . IEEE, 2017, pp. 2043–2050
2050
Closest in time.