Fetching the paper…
Reading the bibliography…
In the realm of robotics, the quest for achieving real-world autonomy, capable of executing large-scale and long-term operations, has positioned place recognition (PR) as a cornerstone technology.
C. Chow and C. Liu, “Approximating discrete probability distributions with dependence trees,” IEEE transactions on Information Theory , vol. 14, no. 3, pp. 462–467, 1968
1968
Earlier work this paper cites.
J. O’Keefe, “Place units in the hippocampus of the freely moving rat,” Experimental neurology , vol. 51, no. 1, pp. 78–109, 1976
1976
Earlier work this paper cites.
W. Maass, “Networks of spiking neurons: the third generation of neural network models,” Neural networks , vol. 10, no. 9, pp. 1659–1671, 1997
1997
Earlier work this paper cites.
H. Bay, T. Tuytelaars, and L. Van Gool, “SURF: Speeded up robust features,” in Computer Vision–ECCV 2006: 9th European Conference on Computer Vision, Graz, Austria, May 7-13, 2006. Proceedings, Part I 9 . Springer, 2006, pp. 404–417
2006
Earlier work this paper cites.
A. Oliva and A. Torralba, “Building the gist of a scene: The role of global image features in recognition,” Progress in brain research , vol. 155, pp. 23–36, 2006
2006
Earlier work this paper cites.
D. Gálvez-López and J. D. Tardos, “Bags of binary words for fast place recognition in image sequences,” IEEE Transactions on Robotics , vol. 28, no. 5, pp. 1188–1197, 2012
2012
Earlier work this paper cites.
M. J. Milford and G. F. Wyeth, “Seqslam: Visual route-based navigation for sunny summer days and stormy winter nights,” in 2012 IEEE International Conference on Robotics and Automation , 2012, pp. 1643–1649
2012
Earlier work this paper cites.
M. Cuturi, “Sinkhorn distances: Lightspeed computation of optimal transport,” Advances in neural information processing systems , vol. 26, 2013
2013
Earlier work this paper cites.
E. Stumm, C. Mei, and S. Lacroix, “Probabilistic place recognition with covisibility maps,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems . IEEE, 2013, pp. 4158–4163
2013
Earlier work this paper cites.
J. Shotton, B. Glocker, C. Zach, S. Izadi, A. Criminisi, and A. Fitzgibbon, “Scene coordinate regression forests for camera relocalization in rgb-d images,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2013, pp. 2930–2937
2013
Earlier work this paper cites.
Y. Liu and H. Zhang, “Towards improving the efficiency of sequence-based slam,” in 2013 IEEE International Conference on Mechatronics and Automation , 2013, pp. 1261–1266
2013
Earlier work this paper cites.
G. D. Tipaldi, D. Meyer-Delius, and W. Burgard, “Lifelong localization in changing environments,” The International Journal of Robotics Research , vol. 32, no. 14, pp. 1662–1678, 2013
2013
Earlier work this paper cites.
N. Sünderhauf, P. Neubert, and P. Protzel, “Are we there yet? challenging seqslam on a 3000 km journey across all four seasons,” in Proc. of workshop on long-term autonomy, IEEE international conference on robotics and automation (ICRA) , 2013, p. 2013
2013
Earlier work this paper cites.
A. Torii, J. Sivic, T. Pajdla, and M. Okutomi, “Visual place recognition with repetitive structures,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2013, pp. 883–890
2013
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: The kitti dataset,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1231–1237, 2013
2013
Earlier work this paper cites.
D. Scaramuzza, Omnidirectional Camera . Boston, MA: Springer US, 2014, pp. 552–560
2014
Earlier work this paper cites.
P. Hansen and B. Browning, “Visual place recognition using hmm sequence matchingv,” in 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems , 2014, pp. 4549–4555
2014
Earlier work this paper cites.
E. Pepperell, P. I. Corke, and M. J. Milford, “All-environment visual place recognition with smart,” in 2014 IEEE international conference on robotics and automation (ICRA) . IEEE, 2014, pp. 1612–1618
2014
Earlier work this paper cites.
S. Lowry, N. Sünderhauf, P. Newman, J. J. Leonard, D. Cox, P. Corke, and M. J. Milford, “Visual place recognition: A survey,” ieee transactions on robotics , vol. 32, no. 1, pp. 1–19, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
A. Torii, R. Arandjelović, J. Sivic, M. Okutomi, and T. Pajdla, “24/7 place recognition by view synthesis,” in 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2015, pp. 1808–1817
2015
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings , Y. Bengio and Y. LeCun, Eds., 2015
2015
Earlier work this paper cites.
C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, J. Neira, I. Reid, and J. J. Leonard, “Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age,” IEEE Transactions on Robotics , vol. 32, no. 6, pp. 1309–1332, 2016
2016
Earlier work this paper cites.
R. Arandjelovic, P. Gronat, A. Torii, T. Pajdla, and J. Sivic, “Netvlad: Cnn architecture for weakly supervised place recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2016, pp. 5297–5307
2016
Earlier work this paper cites.
E. Stumm, C. Mei, S. Lacroix, J. Nieto, M. Hutter, and R. Siegwart, “Robust visual place recognition with graph kernels,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 4535–4544
2016
Earlier work this paper cites.
L. He, X. Wang, and H. Zhang, “M2dp: A novel 3d point cloud descriptor and its application in loop closure detection,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2016, pp. 231–237
2016
Earlier work this paper cites.
Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in International Conference on Machine Learning , 2016, pp. 1050–1059
2016
Earlier work this paper cites.
L. Carlone, G. C. Calafiore, C. Tommolillo, and F. Dellaert, “Planar pose graph optimization: Duality, optimal solutions, and verification,” IEEE Transactions on Robotics , vol. 32, no. 3, pp. 545–565, 2016
2016
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Commun. ACM , vol. 60, no. 6, pp. 84–90, may 2017
2017
Earlier work this paper cites.
B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba, “Places: A 10 million image database for scene recognition,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 6, pp. 1452–1464, 2017
2017
Earlier work this paper cites.
R. Q. Charles, H. Su, M. Kaichun, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2017, pp. 77–85
2017
Earlier work this paper cites.
S. M. Siam and H. Zhang, “Fast-seqslam: A fast appearance based place recognition algorithm,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) , 2017, pp. 5702–5708
2017
Earlier work this paper cites.
B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
S. M. Siam and H. Zhang, “Fast-seqslam: A fast appearance based place recognition algorithm,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) , 2017, pp. 5702–5708
2017
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. Kendall and Y. Gal, “What uncertainties do we need in bayesian deep learning for computer vision?” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
W. Maddern, G. Pascoe, C. Linegar, and P. Newman, “1 year, 1000 km: The oxford robotcar dataset,” The International Journal of Robotics Research , vol. 36, no. 1, pp. 3–15, 2017
2017
Earlier work this paper cites.
F. Radenović, G. Tolias, and O. Chum, “Fine-tuning cnn image retrieval with no human annotation,” IEEE transactions on pattern analysis and machine intelligence , vol. 41, no. 7, pp. 1655–1668, 2018
2018
Earlier work this paper cites.
G. Kim and A. Kim, “Scan context: Egocentric spatial descriptor for place recognition within 3d point cloud map,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018, pp. 4802–4809
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
M. A. Uy and G. H. Lee, “Pointnetvlad: Deep point cloud based retrieval for large-scale place recognition,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2018, pp. 4470–4479
2018
Earlier work this paper cites.
S. Garg, N. Suenderhauf, and M. Milford, “Don’t look back: Robustifying place categorization for viewpoint- and condition-invariant place recognition,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) , 2018, pp. 3645–3652
2018
Earlier work this paper cites.
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 4510–4520
2018
Earlier work this paper cites.
L. Bampis, A. Amanatiadis, and A. Gasteratos, “Fast loop-closure detection using visual-word-vectors from image sequences,” The International Journal of Robotics Research , vol. 37, no. 1, pp. 62–82, 2018
2018
Earlier work this paper cites.
M. Sensoy, L. Kaplan, and M. Kandemir, “Evidential deep learning to quantify classification uncertainty,” Advances in neural information processing systems , vol. 31, 2018
2018
Earlier work this paper cites.
T. Qin, P. Li, and S. Shen, “Vins-mono: A robust and versatile monocular visual-inertial state estimator,” IEEE Transactions on Robotics , vol. 34, no. 4, pp. 1004–1020, 2018
2018
Earlier work this paper cites.
W. Chen, L. Zhu, Y. Guan, C. R. Kube, and H. Zhang, “Submap-based pose-graph visual slam: A robust visual exploration and localization system,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 6851–6856
2018
Earlier work this paper cites.
N. Piasco, D. Sidibé, V. Gouet-Brunet, and C. Demonceaux, “Learning scene geometry for visual localization in challenging conditions,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 9094–9100
2019
Earlier work this paper cites.
C. Choy, J. Gwak, and S. Savarese, “4d spatio-temporal convnets: Minkowski convolutional neural networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 3075–3084
2019
Earlier work this paper cites.
Z. Liu, S. Zhou, C. Suo, P. Yin, W. Chen, H. Wang, H. Li, and Y. Liu, “Lpd-net: 3d point cloud learning for large-scale place recognition and environment analysis,” in 2019 IEEE/CVF International Conference on Computer Vision (ICCV) , 2019, pp. 2831–2840
2019
Earlier work this paper cites.
Z. Hong, Y. Petillot, D. Lane, Y. Miao, and S. Wang, “Textplace: Visual place recognition and topological localization through reading scene texts,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 2861–2870
2019
Earlier work this paper cites.
Y. Shi and A. K. Jain, “Probabilistic face embeddings,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 6902–6911
2019
Earlier work this paper cites.
N. Merrill and G. Huang, “Calc2.0: Combining appearance, semantic and geometric information for robust and efficient visual loop closure,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2019, pp. 4554–4561
2019
Earlier work this paper cites.
P. Yin, L. Xu, X. Li, C. Yin, Y. Li, R. A. Srivatsan, L. Li, J. Ji, and Y. He, “A multi-domain feature learning method for visual place recognition,” in 2019 International Conference on Robotics and Automation (ICRA) , 2019, pp. 319–324
2019
Earlier work this paper cites.
Z. Liu, C. Suo, S. Zhou, F. Xu, H. Wei, W. Chen, H. Wang, X. Liang, and Y.-H. Liu, “Seqlpd: Sequence matching enhanced loop-closure detection based on large-scale point cloud description for self-driving vehicles,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2019, pp. 1218–1223
2019
Earlier work this paper cites.
P. Yin, R. A. Srivatsan, Y. Chen, X. Li, H. Zhang, L. Xu, L. Li, Z. Jia, J. Ji, and Y. He, “Mrs-vpr: a multi-resolution sampling based global visual place recognition method,” in 2019 International Conference on Robotics and Automation (ICRA) , 2019, pp. 7137–7142
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 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 12 708–12 717
2019
Earlier work this paper cites.
L. Liu and H. Li, “Lending orientation to neural networks for cross-view geo-localization,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2019, pp. 5617–5626
2019
Earlier work this paper cites.
D. Van Opdenbosch and E. Steinbach, “Collaborative visual slam using compressed feature exchange,” IEEE Robotics and Automation Letters , vol. 4, no. 1, pp. 57–64, 2019
2019
Earlier work this paper cites.
M. Labbé and F. Michaud, “Rtab-map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation,” Journal of Field Robotics , vol. 36, no. 2, pp. 416–446, 2019
2019
Earlier work this paper cites.
T. Weyand, A. Araujo, B. Cao, and J. Sim, “Google landmarks dataset v2 - a large-scale benchmark for instance-level recognition and retrieval,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 2572–2581
2020
Earlier work this paper cites.
F. Warburg, S. Hauberg, M. López-Antequera, P. Gargallo, Y. Kuang, and J. Civera, “Mapillary street-level sequences: A dataset for lifelong place recognition,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2020, pp. 2623–2632
2020
Earlier work this paper cites.
M. Zaffar, S. Ehsan, M. Milford, and K. McDonald-Maier, “Cohog: A light-weight, compute-efficient, and training-free visual place recognition technique for changing environments,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 1835–1842, 2020
2020
Earlier work this paper cites.
A. Oertel, T. Cieslewski, and D. Scaramuzza, “Augmenting visual place recognition with structural cues,” IEEE Robotics and Automation Letters , vol. 5, no. 4, pp. 5534–5541, 2020
2020
Earlier work this paper cites.
Y. Wang, Z. Sun, C.-Z. Xu, S. E. Sarma, J. Yang, and H. Kong, “Lidar iris for loop-closure detection,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 5769–5775
2020
Earlier work this paper cites.
S. Saftescu, M. Gadd, D. De Martini, D. Barnes, and P. Newman, “Kidnapped radar: Topological radar localisation using rotationally-invariant metric learning,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020, pp. 4358–4364
2020
Earlier work this paper cites.
M. Gadd, D. De Martini, and P. Newman, “Look around you: Sequence-based radar place recognition with learned rotational invariance,” in 2020 IEEE/ION Position, Location and Navigation Symposium (PLANS) , 2020, pp. 270–276
2020
Earlier work this paper cites.
X. Kong, X. Yang, G. Zhai, X. Zhao, X. Zeng, M. Wang, Y. Liu, W. Li, and F. Wen, “Semantic graph based place recognition for 3d point clouds,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 8216–8223
2020
Earlier work this paper cites.
P. Yin, F. Wang, A. Egorov, J. Hou, J. Zhang, and H. Choset, “Seqspherevlad: Sequence matching enhanced orientation-invariant place recognition,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020, pp. 5024–5029
2020
Earlier work this paper cites.
S. Garg and M. Milford, “Fast, compact and highly scalable visual place recognition through sequence-based matching of overloaded representations,” in 2020 IEEE international conference on robotics and automation (ICRA) . IEEE, 2020, pp. 3341–3348
2020
Earlier work this paper cites.
B. Patel, T. D. Barfoot, and A. P. Schoellig, “Visual localization with google earth images for robust global pose estimation of uavs,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 6491–6497
2020
Earlier work this paper cites.
A. Witze et al. , “Nasa has launched the most ambitious mars rover ever built: Here’s what happens next,” Nature , vol. 584, no. 7819, pp. 15–16, 2020
2020
Earlier work this paper cites.
T. Sasaki, K. Otsu, R. Thakker, S. Haesaert, and A.-a. Agha-mohammadi, “Where to map? iterative rover-copter path planning for mars exploration,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 2123–2130, 2020
2020
Earlier work this paper cites.
K. Ebadi, Y. Chang, M. Palieri, A. Stephens, A. Hatteland, E. Heiden, A. Thakur, N. Funabiki, B. Morrell, S. Wood, L. Carlone, and A.-a. Agha-mohammadi, “Lamp: Large-scale autonomous mapping and positioning for exploration of perceptually-degraded subterranean environments,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020, pp. 80–86
2020
Earlier work this paper cites.
D. Barnes, M. Gadd, P. Murcutt, P. Newman, and I. Posner, “The oxford radar robotcar dataset: A radar extension to the oxford robotcar dataset,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 6433–6438
2020
Cited alongside, same era.
M. Ramezani, Y. Wang, M. Camurri, D. Wisth, M. Mattamala, and M. Fallon, “The newer college dataset: Handheld lidar, inertial and vision with ground truth,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020, pp. 4353–4360
2020
Cited alongside, same era.
G. Kim, Y. S. Park, Y. Cho, J. Jeong, and A. Kim, “Mulran: Multimodal range dataset for urban place recognition,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020, pp. 6246–6253
2020
Cited alongside, same era.
X. Zhang, L. Wang, and Y. Su, “Visual place recognition: A survey from deep learning perspective,” Pattern Recognition , vol. 113, p. 107760, 2021
2021
Cited alongside, same era.
Y. Chen and T. D. Barfoot, “Self-supervised feature learning for long-term metric visual localization,” IEEE Robotics and Automation Letters , vol. 8, no. 2, pp. 472–479, 2022
2022
Later among the works it cites.
L. Ding, R. Zhou, Y. Yuan, H. Yang, J. Li, T. Yu, C. Liu, J. Wang, S. Li, H. Gao, Z. Deng, etc., S. Liu, and K. Di, “A 2-year locomotive exploration and scientific investigation of the lunar farside by the yutu-2 rover,” Science Robotics , vol. 7, no. 62, p. eabj6660, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Tian, Y. Chang, F. H. Arias, C. Nieto-Granda, J. P. How, and L. Carlone, “Kimera-multi: Robust, distributed, dense metric-semantic slam for multi-robot systems,” IEEE Transactions on Robotics , pp. 1–17, 2022
2022
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
2021
Cited alongside, same era.
M. Zaffar, S. Garg, M. Milford, J. Kooij, D. Flynn, K. McDonald-Maier, and S. Ehsan, “Vpr-bench: An open-source visual place recognition evaluation framework with quantifiable viewpoint and appearance change,” Int. J. Comput. Vision , vol. 129, no. 7, pp. 2136–2174, jul 2021
2021
Cited alongside, same era.
B. Mildenhall, P. P. Srinivasan, M. Tancik, J. T. Barron, R. Ramamoorthi, and R. Ng, “Nerf: Representing scenes as neural radiance fields for view synthesis,” Communications of the ACM , vol. 65, no. 1, pp. 99–106, 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
S. Hausler, S. Garg, M. Xu, M. Milford, and T. Fischer, “Patch-netvlad: Multi-scale fusion of locally-global descriptors for place recognition,” in 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2021, pp. 14 136–14 147
2021
Cited alongside, same era.
I. O. Tolstikhin, N. Houlsby, A. Kolesnikov, L. Beyer, X. Zhai, T. Unterthiner, J. Yung, A. Steiner, D. Keysers, J. Uszkoreit et al. , “Mlp-mixer: An all-mlp architecture for vision,” Advances in neural information processing systems , vol. 34, pp. 24 261–24 272, 2021
2021
Cited alongside, same era.
G. Peng, Y. Yue, J. Zhang, Z. Wu, X. Tang, and D. Wang, “Semantic reinforced attention learning for visual place recognition,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 13 415–13 422
2021
Cited alongside, same era.
Later among the works it cites.
H. Xu, Y. Zhang, B. Zhou, L. Wang, X. Yao, G. Meng, and S. Shen, “Omni-Swarm: A decentralized omnidirectional visual–inertial–uwb state estimation system for aerial swarms,” IEEE Transactions on Robotics , vol. 38, no. 6, pp. 3374–3394, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
K. Burnett, D. J. Yoon, Y. Wu, A. Z. Li, H. Zhang, S. Lu, J. Qian, W.-K. Tseng, A. Lambert, K. Y. Leung et al. , “Boreas: A multi-season autonomous driving dataset,” The International Journal of Robotics Research , vol. 42, no. 1-2, pp. 33–42, 2023
2023
Later among the works it cites.
P. Yin, A. Abuduweili, S. Zhao, L. Xu, C. Liu, and S. Scherer, “Bioslam: A bioinspired lifelong memory system for general place recognition,” IEEE Transactions on Robotics , 2023
2023
Later among the works it cites.
A. Ali-Bey, B. Chaib-Draa, and P. Giguere, “MixVPR: Feature mixing for visual place recognition,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision , 2023, pp. 2998–3007
2023
Later among the works it cites.
N. Keetha, A. Mishra, J. Karhade, K. M. Jatavallabhula, S. Scherer, M. Krishna, and S. Garg, “Anyloc: Towards universal visual place recognition,” IEEE Robotics and Automation Letters , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
S. Zhu, L. Yang, C. Chen, M. Shah, X. Shen, and H. Wang, “R2former: Unified retrieval and reranking transformer for place recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 19 370–19 380
2023
Later among the works it cites.
X. Xu, S. Lu, J. Wu, H. Lu, Q. Zhu, Y. Liao, R. Xiong, and Y. Wang, “Ring++: Roto-translation invariant gram for global localization on a sparse scan map,” IEEE Transactions on Robotics , vol. 39, no. 6, pp. 4616–4635, 2023
2023
Later among the works it cites.
L. Luo, S. Zheng, Y. Li, Y. Fan, B. Yu, S.-Y. Cao, J. Li, and H.-L. Shen, “Bevplace: Learning lidar-based place recognition using bird’s eye view images,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8700–8709
2023
Later among the works it cites.
K. Vidanapathirana, P. Moghadam, S. Sridharan, and C. Fookes, “Spectral geometric verification: Re-ranking point cloud retrieval for metric localization,” IEEE Robotics and Automation Letters , vol. 8, no. 5, pp. 2494–2501, 2023
2023
Later among the works it cites.
T. Barros, L. Garrote, M. Aleksandrov, C. Premebida, and U. J. Nunes, “Trer: A lightweight transformer re-ranking approach for 3d lidar place recognition,” in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC) , 2023, pp. 2843–2849
2023
Later among the works it cites.
N. Kim, O. Kwon, H. Yoo, Y. Choi, J. Park, and S. Oh, “Topological semantic graph memory for image-goal navigation,” in Conference on Robot Learning . PMLR, 2023, pp. 393–402
2023
Later among the works it cites.
O. Kwon, J. Park, and S. Oh, “Renderable neural radiance map for visual navigation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 9099–9108
2023
Later among the works it cites.
E. Brachmann, T. Cavallari, and V. A. Prisacariu, “Accelerated coordinate encoding: Learning to relocalize in minutes using rgb and poses,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 5044–5053
2023
Later among the works it cites.
P. Yin, I. Cisneros, S. Zhao, J. Zhang, H. Choset, and S. Scherer, “iSimLoc: Visual global localization for previously unseen environments with simulated images,” IEEE Transactions on Robotics , 2023
2023
Later among the works it cites.
K. Hou, D. Kong, J. Jiang, H. Zhuang, X. Huang, and Z. Fang, “Fe-fusion-vpr: Attention-based multi-scale network architecture for visual place recognition by fusing frames and events,” IEEE Robotics and Automation Letters , vol. 8, no. 6, pp. 3526–3533, 2023
2023
Later among the works it cites.
P. Yin, S. Zhao, H. Lai, R. Ge, J. Zhang, H. Choset, and S. Scherer, “Automerge: A framework for map assembling and smoothing in city-scale environments,” IEEE Transactions on Robotics , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
P. Yun and M. Liu, “Laplace approximation based epistemic uncertainty estimation in 3d object detection,” in Conference on Robot Learning . PMLR, 2023, pp. 1125–1135
2023
Later among the works it cites.
G. Berton, G. Trivigno, B. Caputo, and C. Masone, “Eigenplaces: Training viewpoint robust models for visual place recognition,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 11 080–11 090
2023
Later among the works it cites.
S. Jang and U.-H. Kim, “On the study of data augmentation for visual place recognition,” IEEE Robotics and Automation Letters , 2023
2023
Later among the works it cites.
J. Knights, S. Hausler, S. Sridharan, C. Fookes, and P. Moghadam, “Geoadapt: Self-supervised test-time adaptation in lidar place recognition using geometric priors,” IEEE Robotics and Automation Letters , 2023
2023
Later among the works it cites.
J. Cui and X. Chen, “Ccl: Continual contrastive learning for lidar place recognition,” IEEE Robotics and Automation Letters , vol. 8, no. 8, pp. 4433–4440, 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
K. Mason, J. Knights, M. Ramezani, P. Moghadam, and D. Miller, “Uncertainty-aware lidar place recognition in novel environments,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 3366–3373
2023
Later among the works it cites.
Y. Tian, Y. Chang, L. Quang, A. Schang, C. Nieto-Granda, J. P. How, and L. Carlone, “Resilient and distributed multi-robot visual slam: Datasets, experiments, and lessons learned,” in 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2023, pp. 11 027–11 034
2023
Later among the works it cites.
2023
Later among the works it cites.
D. Adolfsson, M. Karlsson, V. Kubelka, M. Magnusson, and H. Andreasson, “Tbv radar slam–trust but verify loop candidates,” IEEE Robotics and Automation Letters , 2023
2023
Later among the works it cites.
2023
Later among the works it cites.
Y. Shi, F. Wu, A. Perincherry, A. Vora, and H. Li, “Boosting 3-dof ground-to-satellite camera localization accuracy via geometry-guided cross-view transformer,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 21 516–21 526
2023
Later among the works it cites.
2023
Later among the works it cites.
B. Kerbl, G. Kopanas, T. Leimkühler, and G. Drettakis, “3d gaussian splatting for real-time radiance field rendering,” ACM Transactions on Graphics , vol. 42, no. 4, pp. 1–14, 2023
2023
Later among the works it cites.
Tesla, Inc., “Autopilot support,” 2023, accessed: 2023-03-30. [Online]. Available: https://www.tesla.com/en_gb/support/autopilot
2023
Later among the works it cites.
J. Knights, K. Vidanapathirana, M. Ramezani, S. Sridharan, C. Fookes, and P. Moghadam, “Wild-Places: A large-scale dataset for lidar place recognition in unstructured natural environments,” in 2023 IEEE international conference on robotics and automation (ICRA) . IEEE, 2023, pp. 11 322–11 328
2023
Later among the works it cites.
M. Leyva-Vallina, N. Strisciuglio, and N. Petkov, “Data-efficient large scale place recognition with graded similarity supervision,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 23 487–23 496
2023
Later among the works it cites.
2023
Later among the works it cites.
H. Yin, X. Xu, S. Lu, X. Chen, R. Xiong, S. Shen, C. Stachniss, and Y. Wang, “A survey on global lidar localization: Challenges, advances and open problems,” International Journal of Computer Vision , pp. 1–33, 2024
2024
Closest in time.
2024
Closest in time.
F. Lu, L. Zhang, X. Lan, S. Dong, Y. Wang, and C. Yuan, “Towards seamless adaptation of pre-trained models for visual place recognition,” in The Twelfth International Conference on Learning Representations , 2024
2024
Closest in time.
S. Izquierdo and J. Civera, “Optimal transport aggregation for visual place recognition,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 17 658–17 668
2024
Closest in time.
C. Yuan, J. Lin, Z. Liu, H. Wei, X. Hong, and F. Zhang, “Btc: A binary and triangle combined descriptor for 3-d place recognition,” IEEE Transactions on Robotics , vol. 40, pp. 1580–1599, 2024
2024
Closest in time.
C. Meng, Y. Duan, C. He, D. Wang, X. Fan, and Y. Zhang, “mmplace: Robust place recognition with intermediate frequency signal of low-cost single-chip millimeter wave radar,” IEEE Robotics and Automation Letters , 2024
2024
Closest in time.
N. Wang, X. Chen, C. Shi, Z. Zheng, H. Yu, and H. Lu, “Sglc: Semantic graph-guided coarse-fine-refine full loop closing for lidar slam,” IEEE Robotics and Automation Letters , 2024
2024
Closest in time.
C. Kassab, M. Mattamala, L. Zhang, and M. Fallon, “Language-extended indoor slam (lexis): A versatile system for real-time visual scene understanding,” in 2024 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2024, pp. 15 988–15 994
2024
Closest in time.
2024
Closest in time.
J. Chen, D. Barath, I. Armeni, M. Pollefeys, and H. Blum, ““where am i?” scene retrieval with language,” in European Conference on Computer Vision . Springer, 2024, pp. 201–220
2024
Closest in time.
S. Chen, Y. Bhalgat, X. Li, J.-W. Bian, K. Li, Z. Wang, and V. A. Prisacariu, “Neural refinement for absolute pose regression with feature synthesis,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024, pp. 20 987–20 996
2024
Closest in time.
2024
Closest in time.
L. Wang, X. Zhang, H. Su, and J. Zhu, “A comprehensive survey of continual learning: theory, method and application,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2024
2024
Closest in time.
X. Hu, L. Zheng, J. Wu, R. Geng, Y. Yu, H. Wei, X. Tang, L. Wang, J. Jiao, and M. Liu, “PALoc: Advancing slam benchmarking with prior-assisted 6-dof trajectory generation and uncertainty estimation,” IEEE/ASME Transactions on Mechatronics , 2024
2024
Closest in time.
J. Yu, H. Ye, J. Jiao, P. Tan, and H. Zhang, “GV-Bench: Benchmarking local feature matching for geometric verification of long-term loop closure detection,” in 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2024, pp. 7922–7928
2024
Closest in time.
O. Michel, A. Bhattad, E. VanderBilt, R. Krishna, A. Kembhavi, and T. Gupta, “Object 3dit: Language-guided 3d-aware image editing,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
P.-E. Sarlin, E. Trulls, M. Pollefeys, J. Hosang, and S. Lynen, “Snap: Self-supervised neural maps for visual positioning and semantic understanding,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
Closest in time.
I. D. Miller, F. Cladera, T. Smith, C. J. Taylor, and V. Kumar, “Air-ground collaboration with spomp: Semantic panoramic online mapping and planning,” IEEE Transactions on Field Robotics , 2024
2024
Closest in time.
2024
Closest in time.
2024
Closest in time.
Y. Liu, Y. Fu, M. Qin, Y. Xu, B. Xu, F. Chen, B. Goossens, P. Z. Sun, H. Yu, C. Liu et al. , “Botanicgarden: A high-quality dataset for robot navigation in unstructured natural environments,” IEEE Robotics and Automation Letters , 2024
2024
Closest in time.
G. Tian, J. Zhao, Y. Cai, F. Zhang, X. Wang, C. Ye, S. Zlatanova, and T. Feng, “Vni-net: Vector neurons-based rotation-invariant descriptor for lidar place recognition,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 218, pp. 506–517, 2024
2024
Closest in time.
S. Yu, X. Sun, W. Li, C. Wen, Y. Yang, B. Si, G. Hu, and C. Wang, “NIDALoc: Neurobiologically inspired deep lidar localization,” IEEE Transactions on Intelligent Transportation Systems , vol. 25, no. 5, pp. 4278–4289, 2024
2024
Closest in time.
Y. Cheng, J. Jiao, Y. Wang, and D. Kanoulas, “LoGS: Visual localization via gaussian splatting with fewer training images,” in International Conference on Robotics and Automation (ICRA) . IEEE, 2025
2025
Closest in time.
C. Liu, S. Chen, Y. S. Bhalgat, S. HU, M. Cheng, Z. Wang, V. A. Prisacariu, and T. Braud, “GS-CPR: Efficient camera pose refinement via 3d gaussian splatting,” in The Thirteenth International Conference on Learning Representations , 2025. [Online]. Available: https://openreview.net/forum?id=mP7uV59iJM
2025
Closest in time.
J. Zhang, G. Zhu, S. Li, X. Liu, H. Song, X. Tang, and C. Feng, “Multiview scene graph,” Advances in Neural Information Processing Systems , vol. 37, pp. 17 761–17 788, 2025
2025
Closest in time.
J. Jiao, J. He, C. Liu, S. Aegidius, X. Hu, T. Braud, and D. Kanoulas, “LiteVLoc: Map-lite visual localization for image goal navigation,” in International Conference on Robotics and Automation (ICRA) . IEEE, 2025
2025
Closest in time.
K. MacTavish, M. Paton, and T. D. Barfoot, “Visual triage: A bag-of-words experience selector for long-term visual route following,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) , 2017, pp. 2065–2072
2072
Closest in time.
H. Wang, C. Wang, and L. Xie, “Intensity scan context: Coding intensity and geometry relations for loop closure detection,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 2095–2101
2095
Closest in time.