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Visual Simultaneous Localization and Mapping (vSLAM) is a widely used technique in robotics and computer vision that enables a robot to create a map of an unfamiliar environment using a camera sensor while simultaneously tracking its position over time.
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2004
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H. Durrant-Whyte and T. Bailey, “Simultaneous localization and mapping: part i,”
2006
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A. Nüchter, K. Lingemann, J. Hertzberg, and H. Surmann, “6d slam—3d mapping outdoor environments,”
2007
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A. J. Davison, I. D. Reid, N. D. Molton, and O. Stasse, “Monoslam: Real-time single camera slam,”
2007
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2007
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J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, “A benchmark for the evaluation of rgb-d slam systems,” in
2012
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J. Engel, T. Schöps, and D. Cremers, “Lsd-slam: Large-scale direct monocular slam,” in
2014
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2015
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2015
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2016
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2016
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A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Nießner, “Scannet: Richly-annotated 3d reconstructions of indoor scenes,” in
2017
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M. Grupp, “evo: Python package for the evaluation of odometry and slam,”
2017
Cited alongside, same era.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in
2018
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2019
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D. Menini, S. Kumar, M. R. Oswald, E. Sandström, C. Sminchisescu, and L. Van Gool, “A real-time online learning framework for joint 3d reconstruction and semantic segmentation of indoor scenes,”
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,”
2021
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
2022
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2022
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T. Müller, A. Evans, C. Schied, and A. Keller, “Instant neural graphics primitives with a multiresolution hash encoding,”
2022
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2022
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2021
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E. Sucar, S. Liu, J. Ortiz, and A. J. Davison, “imap: Implicit mapping and positioning in real-time,” in
2021
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S. Zhi, T. Laidlow, S. Leutenegger, and A. J. Davison, “In-place scene labelling and understanding with implicit scene representation,” in
2021
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J. Huang, S.-S. Huang, H. Song, and S.-M. Hu, “Di-fusion: Online implicit 3d reconstruction with deep priors,” in
2021
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Z. Teed and J. Deng, “Droid-slam: Deep visual slam for monocular, stereo, and rgb-d cameras,”
2021
Cited alongside, same era.
T. Müller, “tiny-cuda-nn,” 4 2021. [Online]. Available:
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2022
Later among the works it cites.
X. Yang, H. Li, H. Zhai, Y. Ming, Y. Liu, and G. Zhang, “Vox-fusion: Dense tracking and mapping with voxel-based neural implicit representation,” in
2022
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B. Cheng, I. Misra, A. G. Schwing, A. Kirillov, and R. Girdhar, “Masked-attention mask transformer for universal image segmentation,” in
2022
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2022
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A. Kundu, K. Genova, X. Yin, A. Fathi, C. Pantofaru, L. J. Guibas, A. Tagliasacchi, F. Dellaert, and T. Funkhouser, “Panoptic neural fields: A semantic object-aware neural scene representation,” in
2022
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2022
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2023
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