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This paper presents LiteVLoc, a hierarchical visual localization framework that uses a lightweight topo-metric map to represent the environment.
F. Dayoub, T. Morris, B. Upcroft, and P. Corke, “Vision-only autonomous navigation using topometric maps,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2013, pp. 1923–1929
1929
Earlier work this paper cites.
M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the ACM , vol. 24, no. 6, pp. 381–395, 1981
1981
Earlier work this paper cites.
S. Rusinkiewicz and M. Levoy, “Efficient variants of the icp algorithm,” in Proceedings third international conference on 3-D digital imaging and modeling . IEEE, 2001, pp. 145–152
2001
Earlier work this paper cites.
X. Gao, X. Hou, J. Tang, and H.-F. Cheng, “Complete solution classification for the perspective-three-point problem,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 25, pp. 930–943, 2003. [Online]. Available: https://api.semanticscholar.org/CorpusID:15869446
2003
Earlier work this paper cites.
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International journal of computer vision , vol. 60, pp. 91–110, 2004
2004
Earlier work this paper cites.
D. Anguelov, C. Dulong, D. Filip, C. Frueh, S. Lafon, R. Lyon, A. Ogale, L. Vincent, and J. Weaver, “Google street view: Capturing the world at street level,” Computer , vol. 43, no. 6, pp. 32–38, 2010
2010
Earlier work this paper cites.
E. Rublee, V. Rabaud, K. Konolige, and G. Bradski, “Orb: An efficient alternative to sift or surf,” in 2011 International conference on computer vision . Ieee, 2011, pp. 2564–2571
2011
Earlier work this paper cites.
R. Arandjelović, P. Gronát, A. Torii, T. Pajdla, and J. Sivic, “Netvlad: Cnn architecture for weakly supervised place recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 5297–5307, 2015. [Online]. Available: https://api.semanticscholar.org/CorpusID:44604205
2015
Earlier work this paper cites.
A. Kendall, M. Grimes, and R. Cipolla, “Posenet: A convolutional network for real-time 6-dof camera relocalization,” in Proceedings of the IEEE international conference on computer vision , 2015, pp. 2938–2946
2015
Earlier work this paper cites.
T. Sattler, B. Leibe, and L. Kobbelt, “Efficient and effective prioritized matching for large-scale image-based localization,” IEEE transactions on pattern analysis and machine intelligence , vol. 39, no. 9, pp. 1744–1756, 2016
2016
Earlier work this paper cites.
J. L. Schönberger and J.-M. Frahm, “Structure-from-motion revisited,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2016
2016
Earlier work this paper cites.
E. Brachmann, A. Krull, S. Nowozin, J. Shotton, F. Michel, S. Gumhold, and C. Rother, “Dsac — differentiable ransac for camera localization,” 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 2492–2500, 2016. [Online]. Available: https://api.semanticscholar.org/CorpusID:4001530
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
M. Hutter, C. Gehring, D. Jud, A. Lauber, D. Bellicoso, V. Tsounis, J. Hwangbo, K. Bodie, P. Fankhauser, M. Bloesch, R. Diethelm, S. Bachmann, A. Melzer, and M. A. Höpflinger, “Anymal - a highly mobile and dynamic quadrupedal robot,” 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , pp. 38–44, 2016. [Online]. Available: https://api.semanticscholar.org/CorpusID:19007565
2016
Earlier work this paper cites.
H. Noh, A. Araujo, J. Sim, T. Weyand, and B. Han, “Large-scale image retrieval with attentive deep local features,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 3456–3465
2017
Earlier work this paper cites.
A. Gordo, J. Almazan, J. Revaud, and D. Larlus, “End-to-end learning of deep visual representations for image retrieval,” International Journal of Computer Vision , vol. 124, no. 2, pp. 237–254, 2017
2017
Earlier work this paper cites.
A. Kendall and R. Cipolla, “Geometric loss functions for camera pose regression with deep learning,” in IEEE conference on computer vision and pattern recognition , 2017, pp. 5974–5983
2017
Earlier work this paper cites.
Z. Laskar, I. Melekhov, S. Kalia, and J. Kannala, “Camera relocalization by computing pairwise relative poses using convolutional neural network,” in Proceedings of the IEEE International Conference on Computer Vision Workshops , 2017, pp. 929–938
2017
Earlier work this paper cites.
M. Bloesch, “State estimation for legged robots-kinematics, inertial sensing, and computer vision,” Ph.D. dissertation, ETH Zurich, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
H. Taira, M. Okutomi, T. Sattler, M. Cimpoi, M. Pollefeys, J. Sivic, T. Pajdla, and A. Torii, “Inloc: Indoor visual localization with dense matching and view synthesis,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 7199–7209
2018
Earlier work this paper cites.
V. Balntas, S. Li, and V. Prisacariu, “Relocnet: Continuous metric learning relocalisation using neural nets,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 751–767
2018
Earlier work this paper cites.
D. DeTone, T. Malisiewicz, and A. Rabinovich, “Superpoint: Self-supervised interest point detection and description,” in Proceedings of the IEEE conference on computer vision and pattern recognition workshops , 2018, pp. 224–236
2018
Earlier work this paper cites.
——, “Learning less is more - 6D camera localization via 3D surface regression,” in CVPR , 2018
2018
Earlier work this paper cites.
D. Kanoulas, N. G. Tsagarakis, and M. Vona, “rxkinfu: Moving volume kinectfusion for 3d perception and robotics,” in 18th IEEE/RAS International Conference on Humanoid Robots (Humanoids) , 2018
2018
Earlier work this paper cites.
M. Dusmanu, I. Rocco, T. Pajdla, M. Pollefeys, J. Sivic, A. Torii, and T. Sattler, “D2-Net: A trainable cnn for joint description and detection of local features,” in Proceedings of the ieee/cvf conference on computer vision and pattern recognition , 2019, pp. 8092–8101
2019
Earlier work this paper cites.
P.-E. Sarlin, C. Cadena, R. Siegwart, and M. Dymczyk, “From coarse to fine: Robust hierarchical localization at large scale,” in CVPR , 2019
2019
Earlier work this paper cites.
T. Sattler, Q. Zhou, M. Pollefeys, and L. Leal-Taixé, “Understanding the limitations of cnn-based absolute camera pose regression,” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 3297–3307, 2019. [Online]. Available: https://api.semanticscholar.org/CorpusID:81979654
2019
Cited alongside, same era.
D. Kanoulas, N. G. Tsagarakis, and M. Vona, “Curved patch mapping and tracking for irregular terrain modeling: Application to bipedal robot foot placement,” Robotics and Autonomous Systems , 2019
2019
Cited alongside, same era.
2020
Cited alongside, same era.
P.-E. Sarlin, D. DeTone, T. Malisiewicz, and A. Rabinovich, “Superglue: Learning feature matching with graph neural networks,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 4938–4947
A. Moreau, N. Piasco, M. Bennehar, D. V. Tsishkou, B. Stanciulescu, and A. de La Fortelle, “Crossfire: Camera relocalization on self-supervised features from an implicit representation,” 2023 IEEE/CVF International Conference on Computer Vision (ICCV) , pp. 252–262, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:257427144
2023
Later among the works it cites.
J. Liu, Q. Nie, Y. Liu, and C. Wang, “Nerf-loc: Visual localization with conditional neural radiance field,” 2023 IEEE International Conference on Robotics and Automation (ICRA) , pp. 9385–9392, 2023. [Online]. Available: https://api.semanticscholar.org/CorpusID:258180042
2023
Later among the works it cites.
D. Shah, A. Sridhar, A. Bhorkar, N. Hirose, and S. Levine, “GNM: A general navigation model to drive any robot,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 7226–7233
2023
Later among the works it cites.
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2020
Cited alongside, same era.
2020
Cited alongside, same era.
J. Zhang, C. Hu, R. G. Chadha, and S. Singh, “Falco: Fast likelihood-based collision avoidance with extension to human-guided navigation,” Journal of Field Robotics , vol. 37, no. 8, pp. 1300–1313, 2020
2020
Cited alongside, same era.
A. Rosinol, A. Violette, M. Abate, N. Hughes, Y. Chang, J. Shi, A. Gupta, and L. Carlone, “Kimera: From slam to spatial perception with 3d dynamic scene graphs,” The International Journal of Robotics Research , vol. 40, no. 12-14, pp. 1510–1546, 2021
2021
Cited alongside, same era.
M. Xu, N. Sünderhauf, and M. Milford, “Probabilistic visual place recognition for hierarchical localization,” IEEE Robotics Autom. Lett. , vol. 6, pp. 311–318, 2021. [Online]. Available: https://api.semanticscholar.org/CorpusID:229643627
2021
Cited alongside, same era.
Y. Shavit, R. Ferens, and Y. Keller, “Learning multi-scene absolute pose regression with transformers,” in IEEE/CVF International Conference on Computer Vision , 2021, pp. 2733–2742
2021
Cited alongside, same era.
J. Sun, Z. Shen, Y. Wang, H. Bao, and X. Zhou, “LoFTR: Detector-free local feature matching with transformers,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2021, pp. 8922–8931
2021
Cited alongside, same era.
E. Brachmann and C. Rother, “Visual camera re-localization from RGB and RGB-D images using DSAC,” TPAMI , 2021
2021
Cited alongside, same era.
J. Jiao, Y. Zhu, H. Ye, H. Huang, P. Yun, L. Jiang, L. Wang, and M. Liu, “Greedy-based feature selection for efficient lidar slam,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 5222–5228
2021
Cited alongside, same era.
2023
Later among the works it cites.
J. Edstedt, I. Athanasiadis, M. Wadenbäck, and M. Felsberg, “Dkm: Dense kernelized feature matching for geometry estimation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 17 765–17 775
2023
Later among the works it cites.
J. Liu, S. Lyu, D. Hadjivelichkov, V. Modugno, and D. Kanoulas, “Vit-a*: Legged robot path planning using vision transformer a*,” in IEEE-RAS International Conference on Humanoids Robots (Humanoids) , 2023
2023
Later among the works it cites.
2024
Closest in time.
N. Hughes, Y. Chang, S. Hu, R. Talak, R. Abdulhai, J. Strader, and L. Carlone, “Foundations of spatial perception for robotics: Hierarchical representations and real-time systems,” The International Journal of Robotics Research , p. 02783649241229725, 2024
2024
Closest in time.
M. Zaffar, L. Nan, and J. F. Kooij, “On the estimation of image-matching uncertainty in visual place recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 17 743–17 753
2024
Closest in time.
C. Liu, S. Chen, Y. Zhao, H. Huang, V. Prisacariu, and T. Braud, “Hr-apr: Apr-agnostic framework with uncertainty estimation and hierarchical refinement for camera relocalisation,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) , 2024, pp. 8544–8550
2024
Closest in time.
2024
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C. Liu, S. Chen, Y. Bhalgat, S. Hu, Z. Wang, M. Cheng, V. A. Prisacariu, and T. Braud, “Gsloc: Efficient camera pose refinement via 3d gaussian splatting,” 2024. [Online]. Available: https://api.semanticscholar.org/CorpusID:271916288
2024
Closest in time.
T. D. Barfoot, State estimation for robotics . Cambridge University Press, 2024
2024
Closest in time.
H. Xu, P. Liu, X. Chen, and S. Shen, “D2SLAM: Decentralized and distributed collaborative visual-inertial slam system for aerial swarm,” IEEE Transactions on Robotics , 2024
2024
Closest in time.
C. Liu, Y. Zhao, and T. Braud, “Marvin: Mobile ar dataset with visual-inertial data,” in 2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) . IEEE, 2024, pp. 532–538
2024
Closest in time.
2024
Closest in time.
J. Edstedt, Q. Sun, G. Bökman, M. Wadenbäck, and M. Felsberg, “RoMa: Robust dense feature matching,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 19 790–19 800
2024
Closest in time.
S. Wang, V. Leroy, Y. Cabon, B. Chidlovskii, and J. Revaud, “Dust3r: Geometric 3d vision made easy,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 20 697–20 709
2024
Closest in time.
Y. Wang, X. He, S. Peng, D. Tan, and X. Zhou, “Efficient LoFTR: Semi-dense local feature matching with sparse-like speed,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 21 666–21 675
2024
Closest in time.
G. Potje, F. Cadar, A. Araujo, R. Martins, and E. R. Nascimento, “XFeat: Accelerated features for lightweight image matching,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 2682–2691
2024
Closest in time.
2024
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J. He, H. Huang, S. Zhang, J. Jiao, C. Liu, and M. Liu, “Accurate prior-centric monocular positioning with offline lidar fusion,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 11 934–11 940
2024
Closest in time.
2024
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J. Liu, M. Stamatopoulou, and D. Kanoulas, “Dipper: Diffusion-based 2d path planner applied on legged robots,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 9264–9270
2024
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M. Stamatopoulou, J. Liu, and D. Kanoulas, “Dippest: Diffusion-based path planner for synthesizing trajectories applied on quadruped robots,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2024
2024
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J. Jiao, R. Geng, Y. Li, R. Xin, B. Yang, J. Wu, L. Wang, M. Liu, R. Fan, and D. Kanoulas, “Real-time metric-semantic mapping for autonomous navigation in outdoor environments,” IEEE Transactions on Automation Science and Engineering (T-ASE) , 2024
2024
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Y. Cheng, J. Jiao, Y. Wang, and D. Kanoulas, “Logs: Visual localization via gaussian splatting with fewer training images,” 2024
2024
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