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SLAM systems based on NeRF have demonstrated superior performance in rendering quality and scene reconstruction for static environments compared to traditional dense SLAM.
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2017
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2017
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“Building maps for autonomous navigation using sparse visual SLAM features,”
Y. Ling and S. Shen, · 2017
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M. R"unz and L. Agapito, “Co-fusion: Real-time segmentation, tracking and fusion of multiple objects,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017, pp. 4471–4478
2017
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2018
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2018
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C. Yu, Z. Liu, X. Liu, F. Xie, Y. Yang, Q. Wei, and Q. Fei, "DS-SLAM: A semantic visual SLAM towards dynamic environments," in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, pp. 1168-1174
2018
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Michael Bloesch, Jan Czarnowski, Ronald Clark, Stefan Leutenegger, and Andrew J Davison. "CodeSLAM—learning a compact, optimisable representation for dense visual SLAM." In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2560-2568. 2018
2018
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B. Bescos, J. M. M. Montiel, and D. Scaramuzza, “DynaSLAM: Tracking, mapping, and inpainting in dynamic scenes,” IEEE Robotics and Automation Letters, vol. 3, no. 4, pp. 4076-4083, 2018
2018
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M. Hosseinzadeh, K. Li, Y. Latif, et al. "Real-time monocular object-model aware sparse SLAM," in 2019 International Conference on Robotics and Automation (ICRA), IEEE, 2019, pp. 7123-7129
2019
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M. Strecke and J. Stuckler, "Em-fusion: Dynamic object-level slam with probabilistic data association," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 5865-5874
2019
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Bad slam: Bundle adjusted direct RGB-D slam
Thomas Schops, Torsten Sattler, and Marc Pollefeys · 2019
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2019
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Y. Yao, Z. Luo, S. Li, T. Shen, T. Fang, and L. Quan, “Recurrent MVSNet for high-resolution multi-view stereo depth inference,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 5525–5534
2019
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Palazzolo, E., Behley, J., Lottes, P., Giguere, P., Stachniss, C., “ReFusion: 3D reconstruction in dynamic environments for RGB-D cameras exploiting residuals,” In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Nov. 2019, pp. 7855-7862
2019
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T. Zhang, H. Zhang, Y. Li, Y. Nakamura, and L. Zhang, "FlowFusion: Dynamic Dense RGB-D SLAM Based on Optical Flow," in 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2020, pp. 7322–7328
2020
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Jan Czarnowski, Tristan Laidlow, Ronald Clark, and Andrew J. Davison, "DeepFactors: Real-Time Probabilistic Dense Monocular SLAM," IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 721–728, 2020
2020
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S. Zakharov, W. Kehl, A. Bhargava, and A. Gaidon. Autolabeling 3d objects with differentiable rendering of sdf shape priors. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 12224–12233, 2020
2020
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Z. J. Du, S. S. Huang, T. J. Mu, et al., “Accurate dynamic SLAM using CRF-based long-term consistency,” IEEE Transactions on Visualization and Computer Graphics, 2020, 28(4), 1745-1757
2020
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Shi, X., Li, D., Zhao, P., et al., “Are we ready for service robots? The OpenLORIS-Scene datasets for lifelong SLAM,” In Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2020, pp. 3139-3145
2020
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NeRF-RPN: A general framework for object detection in NeRFs
Benran Hu, Junkai Huang, Yichen Liu, Yu-Wing Tai, and Chi-Keung Tang · 2022
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Z. Zhu, S. Peng, V. Larsson, W. Xu, H. Bao, Z. Cui, M. R. Oswald, and M. Pollefeys, "Nice-slam: Neural implicit scalable encoding for slam," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12786–12796
2022
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2022
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K. Li, Y. Tang, V.A. Prisacariu, and P.H.S. Torr. "Bnv-fusion: dense 3D reconstruction using bi-level neural volume fusion," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 6166–6175
2022
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E. Sucar, S. Liu, J. Ortiz, and A. J. Davison, "iMAP: Implicit mapping and positioning in real-time," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 6229–6238
2021
Cited alongside, same era.
2021
Cited alongside, same era.
Dex-nerf: Using a neural radiance field to grasp transparent objects
Jeffrey Ichnowski, Yahav Avigal, Justin Kerr, and Ken Goldberg · 2021
Cited alongside, same era.
J. Huang, S.-S. Huang, H. Song, and S.-M. Hu. "Di-fusion: Online implicit 3d reconstruction with deep priors," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 8932–8941
2021
Cited alongside, same era.
J. C. V. Soares, M. Gattass, M. A. Meggiolaro, “Crowd-SLAM: visual SLAM towards crowded environments using object detection,” Journal of Intelligent & Robotic Systems, 2021, 102(2), 50
2021
Cited alongside, same era.
B. Bescos, C. Campos, J.D. Tard’os, and J. Neira. "DynaSLAM II: Tightly-coupled multi-object tracking and SLAM," IEEE Robotics and Automation Letters, vol. 6, no. 3, pp. 5191–5198, 2021
2021
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C.-H. Lin, W.-C. Ma, A. Torralba, and S. Lucey, "Barf: Bundle-adjusting neural radiance fields," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2021, pp. 5741–5751
2021
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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. 16558-16569, 2021
2021
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Z. Liao, Y. Hu, J. Zhang, X. Qi, X. Zhang, and W. Wang. "So-slam: Semantic object slam with scale proportional and symmetrical texture constraints," IEEE Robotics and Automation Letters, vol. 7, no. 2, pp. 4008–4015, 2022
2022
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R. Tian, Y. Zhang, Y. Feng, L. Yang, Z. Cao, S. Coleman, and D. Kerr. "Accurate and Robust Object SLAM with 3D Quadric Landmark Reconstruction in Outdoor Environment," in IEEE Robotics and Automation Letters, pp. 1534-1541, 2022
2022
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Y. Wang, K. Xu, Y. Tian, et al., “DRG-SLAM: A Semantic RGB-D SLAM using Geometric Features for Indoor Dynamic Scene,” In Proceedings of the 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), IEEE, 2022, pp. 1352-1359
2022
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Kangle Deng, Andrew Liu, Jun-Yan Zhu, and Deva Ramanan, "Depth-supervised NeRF: Fewer views and faster training for free," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 12882-12891
2022
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Y.-C. Guo, D. Kang, L. Bao, Y. He, and S.-H. Zhang, "Nerfren: Neural radiance fields with reflections," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022, pp. 18409-18418
2022
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2022
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G. Jocher, A. Chaurasia, A. Stoken, et al., "ultralytics/yolov5: v6.1-TensorRT, TensorFlow edge TPU and OpenVINO export and inference," Zenodo, 2022
2022
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Chen, J., Xu, Y., “DynamicVINS: Visual-Inertial Localization and Dynamic Object Tracking,” In Proceedings of the 2022 China Automation Congress (CAC), IEEE, 2022, pp. 6861-6866
2022
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Cheng, S., Sun, C., Zhang, S., et al., “SG-SLAM: a real-time RGB-D visual SLAM toward dynamic scenes with semantic and geometric information,” IEEE Transactions on Instrumentation and Measurement, 2022, 72: 1-12
2022
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2022
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J. He, M. Li, Y. Wang, et al., “OVD-SLAM: An Online Visual SLAM for Dynamic Environments,” IEEE Sensors Journal, 2023
2023
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T. Deng, H. Xie, J. Wang, W. Chen. "Long-Term Visual Simultaneous Localization and Mapping: Using a Bayesian Persistence Filter-Based Global Map Prediction," IEEE Robotics & Automation Magazine, vol. 30, no. 1, pp. 36-49, 2023
2023
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2023
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2023
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X. Kong, S. Liu, M. Taher, et al., “vmap: Vectorised object map for neural field slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 952-961
2023
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M. Li, J. He, Y. Wang, et al. "End-to-End RGB-D SLAM With Multi-MLPs Dense Neural Implicit Representations," IEEE Robotics and Automation Letters, 2023
2023
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C. M. Chung, Y. C. Tseng, Y. C. Hsu, et al., “Orbeez-SLAM: A Real-Time Monocular Visual SLAM with ORB Features and NeRF-Realized Map,” in 2023 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2023, pp. 9400-9406
2023
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Johari M M, Carta C, Fleuret F. , "Eslam: Efficient dense slam system based on hybrid representation of signed distance fields[," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 17408-17419
2023
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Y. Zhang, F. Tosi, S. Mattoccia, et al. "Go-slam: Global optimization for consistent 3D instant reconstruction," in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023, pp. 3727-3737
2023
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2023
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Hengyi Wang, Jingwen Wang, Lourdes Agapito. "Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM,"in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023, pp. 13293-13302
2023
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Bernhard Kerbl, et al., "3D Gaussian Splatting for Real-Time Radiance Field Rendering," ACM Transactions on Graphics (ToG), 42.4 (2023): 1-14
2023
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2023
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X. Kong, S. Liu, M. Taher, et al., “vmap: Vectorised object map** for neural field slam,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023, pp. 952-961
2023
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Ultralytics YOLOv9 GitHub repository, https://github.com/WongKinYiu/yolov9, Accessed 2024
2024
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