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Simultaneous Localization and Mapping (SLAM) with dense representation plays a key role in robotics, Virtual Reality (VR), and Augmented Reality (AR) applications.
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Segal, A., Haehnel, D., Thrun, S.: Generalized-icp. In: Robotics: science and systems. vol. 2, p. 435. Seattle, WA (2009)
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Bylow, E., Sturm, J., Kerl, C., Kahl, F., Cremers, D.: Real-time camera tracking and 3d reconstruction using signed distance functions. In: Robotics: Science and Systems. vol. 2, p. 2 (2013)
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Kerl, C., Sturm, J., Cremers, D.: Dense visual slam for rgb-d cameras. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems. pp. 2100–2106. IEEE (2013)
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Whelan, T., Johannsson, H., Kaess, M., Leonard, J.J., McDonald, J.: Robust real-time visual odometry for dense rgb-d mapping. In: 2013 IEEE International Conference on Robotics and Automation. pp. 5724–5731. IEEE (2013)
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Mallios, A., Ridao, P., Ribas, D., Hernández, E.: Scan matching slam in underwater environments. Autonomous Robots 36
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Zhang, J., Singh, S.: Loam: Lidar odometry and mapping in real-time. In: Robotics: Science and systems. vol. 2, pp. 1–9. Berkeley, CA (2014)
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Ceriani, S., Sánchez, C., Taddei, P., Wolfart, E., Sequeira, V.: Pose interpolation slam for large maps using moving 3d sensors. In: 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS). pp. 750–757. IEEE (2015)
2015
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Mur-Artal, R., Montiel, J.M.M., Tardos, J.D.: Orb-slam: a versatile and accurate monocular slam system. IEEE transactions on robotics 31
2015
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Serafin, J., Grisetti, G.: Nicp: Dense normal based point cloud registration. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 742–749. IEEE (2015)
2015
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Dai, A., Nießner, M., Zollhöfer, M., Izadi, S., Theobalt, C.: Bundlefusion: Real-time globally consistent 3d reconstruction using on-the-fly surface reintegration. ACM Transactions on Graphics (ToG) 36
2017
Cited alongside, same era.
Mur-Artal, R., Tardós, J.D.: Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras. IEEE transactions on robotics 33
2017
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Sucar, E., Liu, S., Ortiz, J., Davison, A.J.: imap: Implicit mapping and positioning in real-time. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 6229–6238 (2021)
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Müller, T., Evans, A., Schied, C., Keller, A.: Instant neural graphics primitives with a multiresolution hash encoding. ACM Transactions on Graphics (ToG) 41
2022
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Yang, X., Li, H., Zhai, H., Ming, Y., Liu, Y., Zhang, G.: Vox-fusion: Dense tracking and mapping with voxel-based neural implicit representation. In: 2022 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). pp. 499–507. IEEE (2022)
2022
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Zhu, Z., Peng, S., Larsson, V., Xu, W., Bao, H., Cui, Z., Oswald, M.R., Pollefeys, M.: Nice-slam: Neural implicit scalable encoding for slam. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 12786–12796 (2022)
2022
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2017
Cited alongside, same era.
Qin, T., Li, P., Shen, S.: Vins-mono: A robust and versatile monocular visual-inertial state estimator. IEEE Transactions on Robotics 34
2018
Cited alongside, same era.
Shan, T., Englot, B.: Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). pp. 4758–4765. IEEE (2018)
2018
Cited alongside, same era.
Taud, H., Mas, J.: Multilayer perceptron (mlp). Geomatic approaches for modeling land change scenarios pp. 451–455 (2018)
2018
Cited alongside, same era.
2019
Cited alongside, same era.
Czarnowski, J., Laidlow, T., Clark, R., Davison, A.J.: Deepfactors: Real-time probabilistic dense monocular slam. IEEE Robotics and Automation Letters 5
2020
Cited alongside, same era.
Lin, J., Zhang, F.: Loam livox: A fast, robust, high-precision lidar odometry and mapping package for lidars of small fov. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). pp. 3126–3131. IEEE (2020)
2020
Cited alongside, same era.
Palieri, M., Morrell, B., Thakur, A., Ebadi, K., Nash, J., Chatterjee, A., Kanellakis, C., Carlone, L., Guaragnella, C., Agha-Mohammadi, A.a.: Locus: A multi-sensor lidar-centric solution for high-precision odometry and 3d mapping in real-time. IEEE Robotics and Automation Letters 6
2020
Cited alongside, same era.
Chung, C.M., Tseng, Y.C., Hsu, Y.C., Shi, X.Q., Hua, Y.H., Yeh, J.F., Chen, W.C., Chen, Y.T., Hsu, W.H.: Orbeez-slam: A real-time monocular visual slam with orb features and nerf-realized mapping. In: 2023 IEEE International Conference on Robotics and Automation (ICRA). pp. 9400–9406. IEEE (2023)
2023
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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. pp. 17408–17419 (2023)
2023
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2023
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Kerbl, B., Kopanas, G., Leimkühler, T., Drettakis, G.: 3d gaussian splatting for real-time radiance field rendering. ACM Transactions on Graphics 42
2023
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Kong, X., Liu, S., Taher, M., Davison, A.J.: vmap: Vectorised object mapping for neural field slam. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 952–961 (2023)
2023
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2023
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Sandström, E., Li, Y., Van Gool, L., Oswald, M.R.: Point-slam: Dense neural point cloud-based slam. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 18433–18444 (2023)
2023
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Vizzo, I., Guadagnino, T., Mersch, B., Wiesmann, L., Behley, J., Stachniss, C.: Kiss-icp: In defense of point-to-point icp–simple, accurate, and robust registration if done the right way. IEEE Robotics and Automation Letters 8
2023
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2023
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