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Visual Simultaneous Localization and Mapping (V-SLAM) methods achieve remarkable performance in static environments, but face challenges in dynamic scenes where moving objects severely affect their core modules.
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Bescos, B., Fácil, J.M., Civera, J., Neira, J.: Dynaslam: Tracking, mapping, and inpainting in dynamic scenes. IEEE Robotics and Automation Letters 3
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Zhong, F., Wang, S., Zhang, Z., Chen, C., Wang, Y.: Detect-slam: Making object detection and slam mutually beneficial. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). pp. 1001–1010 (2018). https://doi.org/10.1109/WACV.2018.00115
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Jinyu, L., Bangbang, Y., Danpeng, C., Nan, W., Guofeng, Z., Hujun, B.: Survey and evaluation of monocular visual-inertial slam algorithms for augmented reality. Virtual Reality & Intelligent Hardware 1
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Liu, X., Qi, C.R., Guibas, L.J.: Flownet3d: Learning scene flow in 3d point clouds. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
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Ranjan, A., Jampani, V., Balles, L., Kim, K., Sun, D., Wulff, J., Black, M.J.: Competitive collaboration: Joint unsupervised learning of depth, camera motion, optical flow and motion segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (June 2019)
Cheng, S., Sun, C., Zhang, S., Zhang, D.: Sg-slam: A real-time rgb-d visual slam toward dynamic scenes with semantic and geometric information. IEEE Transactions on Instrumentation and Measurement 72
2022
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Hu, X., Zhang, Y., Cao, Z., Ma, R., Wu, Y., Deng, Z., Sun, W.: Cfp-slam: A real-time visual slam based on coarse-to-fine probability in dynamic environments. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 4399–4406 (2022). https://doi.org/10.1109/IROS47612.2022.9981826
2022
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Li, Z., Lu, C.Z., Qin, J., Guo, C.L., Cheng, M.M.: Towards an end-to-end framework for flow-guided video inpainting. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2022)
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2019
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Cheng, J., Zhang, H., Meng, M.Q.H.: Improving visual localization accuracy in dynamic environments based on dynamic region removal. IEEE Transactions on Automation Science and Engineering 17
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Niklaus, S., Liu, F.: Softmax splatting for video frame interpolation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 5437–5446 (2020)
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Shi, X., Li, D., Zhao, P., Tian, Q., Tian, Y., Long, Q., Zhu, C., Song, J., Qiao, F., Song, L., et al.: Are we ready for service robots? the openloris-scene datasets for lifelong slam. In: 2020 IEEE international conference on robotics and automation (ICRA). pp. 3139–3145. IEEE (2020)
2020
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Wang, C., Luo, B., Zhang, Y., Zhao, Q., Yin, L., Wang, W., Su, X., Wang, Y., Li, C.: Dymslam: 4d dynamic scene reconstruction based on geometrical motion segmentation. IEEE Robotics and Automation Letters 6
2020
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Wang, W., Zhu, D., Wang, X., Hu, Y., Qiu, Y., Wang, C., Hu, Y., Kapoor, A., Scherer, S.: Tartanair: A dataset to push the limits of visual slam. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 4909–4916 (2020). https://doi.org/10.1109/IROS45743.2020.9341801
2020
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Zhang, T., Zhang, H., Li, Y., Nakamura, Y., Zhang, L.: Flowfusion: Dynamic dense rgb-d slam based on optical flow. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). pp. 7322–7328 (2020). https://doi.org/10.1109/ICRA40945.2020.9197349
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Ballester, I., Fontán, A., Civera, J., Strobl, K.H., Triebel, R.: Dot: Dynamic object tracking for visual slam. In: 2021 IEEE International Conference on Robotics and Automation (ICRA). pp. 11705–11711 (2021). https://doi.org/10.1109/ICRA48506.2021.9561452
2021
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Bescos, B., Campos, C., Tardós, J.D., Neira, J.: Dynaslam ii: Tightly-coupled multi-object tracking and slam. IEEE Robotics and Automation Letters 6
2021
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Liu, J., Li, X., Liu, Y., Chen, H.: Rgb-d inertial odometry for a resource-restricted robot in dynamic environments. IEEE Robotics and Automation Letters 7
2022
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Liu, Y., Zhou, Z.: Optical flow-based stereo visual odometry with dynamic object detection. IEEE Transactions on Computational Social Systems pp. 1–13 (2022). https://doi.org/10.1109/TCSS.2022.3205015
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Wang, Y., Xu, K., Tian, Y., Ding, X.: Drg-slam: A semantic rgb-d slam using geometric features for indoor dynamic scene. In: 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 1352–1359 (2022). https://doi.org/10.1109/IROS47612.2022.9981238
2022
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Xing, Z., Zhu, X., Dong, D.: De-slam: Slam for highly dynamic environment. Journal of Field Robotics 39
2022
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Zhang, J., Gao, M., He, Z., Yang, Y.: Dcs-slam: A semantic slam with moving cluster towards dynamic environments. In: 2022 IEEE International Conference on Robotics and Biomimetics (ROBIO). pp. 1923–1928 (2022). https://doi.org/10.1109/ROBIO55434.2022.10011980
2022
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Zhong, Y., Hu, S., Huang, G., Bai, L., Li, Q.: Wf-slam: A robust vslam for dynamic scenarios via weighted features. IEEE Sensors Journal 22
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2023
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Bonetto, E., Xu, C., Ahmad, A.: Simulation of Dynamic Environments for SLAM. In: ICRA2023 Workshop on Active Methods in Autonomous Navigation (2023)
2023
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He, J., Li, M., Wang, Y., Wang, H.: Ovd-slam: An online visual slam for dynamic environments. IEEE Sensors Journal 23
2023
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Liu, H., Tian, L., Du, Q., Xu, W.: Robust rgb: D-slam in highly dynamic environments based on probability observations and clustering optimization. Measurement Science and Technology 35
2023
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Mirzaei, A., Aumentado-Armstrong, T., Derpanis, K.G., Kelly, J., Brubaker, M.A., Gilitschenski, I., Levinshtein, A.: SPIn-NeRF: Multiview segmentation and perceptual inpainting with neural radiance fields. In: CVPR (2023)
2023
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Shen, S., Cai, Y., Wang, W., Scherer, S.: Dytanvo: Joint refinement of visual odometry and motion segmentation in dynamic environments. In: 2023 IEEE International Conference on Robotics and Automation (ICRA). pp. 4048–4055. IEEE (2023)
2023
Closest in time.