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Robot localization using a built map is essential for a variety of tasks including accurate navigation and mobile manipulation.
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Y. Ma, Y. Guo, J. Zhao, M. Lu, J. Zhang, and J. Wan, “Fast and accurate registration of structured point clouds with small overlaps,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2016, pp. 1–9
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Z. Taylor and J. Nieto, “Motion-based calibration of multimodal sensor extrinsics and timing offset estimation,” IEEE Trans. Robot. , vol. 32, no. 5, pp. 1215–1229, 2016
2016
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2017
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C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” in Proc. Adv. Neural Inf. Process. Syst. , 2017, pp. 5099–5108
2017
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C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 652–660
2017
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K. Yuan, Z. Guo, and Z. J. Wang, “Rggnet: Tolerance aware lidar-camera online calibration with geometric deep learning and generative model,” IEEE Robot. Autom. Lett. , vol. 5, no. 4, pp. 6956–6963, 2020
2020
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Q. Hu, B. Yang, L. Xie, S. Rosa, Y. Guo, Z. Wang, N. Trigoni, and A. Markham, “Randla-net: Efficient semantic segmentation of large-scale point clouds,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 11 108–11 117
2020
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X. Bai, Z. Luo, L. Zhou, H. Fu, L. Quan, and C.-L. Tai, “D3feat: Joint learning of dense detection and description of 3d local features,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2020, pp. 6359–6367
2020
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D. Cattaneo, M. Vaghi, S. Fontana, A. L. Ballardini, and D. G. Sorrenti, “Global visual localization in lidar-maps through shared 2d-3d embedding space,” in Proc. IEEE Int. Conf. Robot. Autom. , 2020, pp. 4365–4371
2020
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G. Elbaz, T. Avraham, and A. Fischer, “3d point cloud registration for localization using a deep neural network auto-encoder,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2017, pp. 4631–4640
2017
Cited alongside, same era.
D. Sun, X. Yang, M.-Y. Liu, and J. Kautz, “Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2018, pp. 8934–8943
2018
Cited alongside, same era.
G. Iyer, R. K. Ram, J. K. Murthy, and K. M. Krishna, “Calibnet: Geometrically supervised extrinsic calibration using 3d spatial transformer networks,” in Proc. Int. Conf. Intell. Robots Syst. , 2018, pp. 1110–1117
2018
Cited alongside, same era.
B. Wu, A. Wan, X. Yue, and K. Keutzer, “Squeezeseg: Convolutional neural nets with recurrent crf for real-time road-object segmentation from 3d lidar point cloud,” in Proc. IEEE Int. Conf. Robot. Autom. , 2018, pp. 1887–1893
2018
Cited alongside, same era.
Z. J. Yew and G. H. Lee, “3dfeat-net: Weakly supervised local 3d features for point cloud registration,” in Proc. Eur. Conf. Comput. Vis. , 2018, pp. 607–623
2018
Cited alongside, same era.
Z. Liu, S. Zhou, C. Suo, P. Yin, W. Chen, H. Wang, H. Li, and Y.-H. Liu, “Lpd-net: 3d point cloud learning for large-scale place recognition and environment analysis,” in Proc. Int. Conf. Comput. Vis. , 2019, pp. 2831–2840
2019
Cited alongside, same era.
W. Lu, G. Wan, Y. Zhou, X. Fu, P. Yuan, and S. Song, “Deepvcp: An end-to-end deep neural network for point cloud registration,” in Proc. Int. Conf. Comput. Vis. , 2019, pp. 12–21
2019
Cited alongside, same era.
M. Feng, S. Hu, M. H. Ang, and G. H. Lee, “2d3d-matchnet: Learning to match keypoints across 2d image and 3d point cloud,” in Proc. IEEE Int. Conf. Robot. Autom. , 2019, pp. 4790–4796
2019
Cited alongside, same era.
B. Wang, C. Chen, Z. Cui, J. Qin, C. X. Lu, Z. Yu, P. Zhao, Z. Dong, F. Zhu, N. Trigoni et al. , “P2-net: Joint description and detection of local features for pixel and point matching,” in Proc. Int. Conf. Comput. Vis. , 2021, pp. 16 004–16 013
2021
Later among the works it cites.
J. Li and G. H. Lee, “Deepi2p: Image-to-point cloud registration via deep classification,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2021, pp. 15 960–15 969
2021
Later among the works it cites.
M.-F. Chang, J. Mangelson, M. Kaess, and S. Lucey, “Hypermap: Compressed 3d map for monocular camera registration,” in Proc. IEEE Int. Conf. Robot. Autom. IEEE, 2021, pp. 11 739–11 745
2021
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J. Yin, A. Li, T. Li, W. Yu, and D. Zou, “M2dgr: A multi-sensor and multi-scenario slam dataset for ground robots,” IEEE Robot. Autom. Lett. , vol. 7, no. 2, pp. 2266–2273, 2021
2021
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J. Houston, G. Zuidhof, L. Bergamini, Y. Ye, L. Chen, A. Jain, S. Omari, V. Iglovikov, and P. Ondruska, “One thousand and one hours: Self-driving motion prediction dataset,” in Conference on Robot Learning . PMLR, 2021, pp. 409–418
2021
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G. Wang, X. Wu, Z. Liu, and H. Wang, “Hierarchical attention learning of scene flow in 3d point clouds,” IEEE Transactions on Image Processing , vol. 30, pp. 5168–5181, 2021
2021
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G. Wang, X. Wu, Z. Liu, and H. Wang, “Pwclo-net: Deep lidar odometry in 3d point clouds using hierarchical embedding mask optimization,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2021, pp. 15 910–15 919
2021
Later among the works it cites.
S. Huang, Z. Gojcic, M. Usvyatsov, A. Wieser, and K. Schindler, “Predator: Registration of 3d point clouds with low overlap,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. , 2021, pp. 4267–4276
2021
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D. Cattaneo, M. Vaghi, and A. Valada, “Lcdnet: Deep loop closure detection and point cloud registration for lidar slam,” IEEE Trans. Robot. , 2022
2022
Later among the works it cites.
S. Ren, Y. Zeng, J. Hou, and X. Chen, “Corri2p: Deep image-to-point cloud registration via dense correspondence,” IEEE Trans. Circuits Syst. Video Technol. , 2022
2022
Later among the works it cites.
Y. Jeon and S.-W. Seo, “Efghnet: A versatile image-to-point cloud registration network for extreme outdoor environment,” IEEE Robot. Autom. Lett. , vol. 7, no. 3, pp. 7511–7517, 2022
2022
Later among the works it cites.
K. Chen, H. Yu, W. Yang, L. Yu, S. Scherer, and G.-S. Xia, “I2d-loc: Camera localization via image to lidar depth flow,” ISPRS Journal of Photogrammetry and Remote Sensing , vol. 194, pp. 209–221, 2022
2022
Later among the works it cites.
G. Wang, Y. Hu, Z. Liu, Y. Zhou, M. Tomizuka, W. Zhan, and H. Wang, “What matters for 3d scene flow network,” in Proc. Eur. Conf. Comput. Vis. , 2022, pp. 38–55
2022
Later among the works it cites.
G. Wang, X. Wu, S. Jiang, Z. Liu, and H. Wang, “Efficient 3d deep lidar odometry,” IEEE Trans. Pattern Anal. Mach. Intell. , 2022
2022
Later among the works it cites.
S. Shubodh, M. Omama, H. Zaidi, U. S. Parihar, and M. Krishna, “Lip-loc: Lidar image pretraining for cross-modal localization,” in Proc. IEEE Winter Conf. Appl. Comput. Vis. Workshops , 2024, pp. 948–957
2022
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H. Qiao, Y.-X. Wu, S.-L. Zhong, P.-J. Yin, and J.-H. Chen, “Brain-inspired intelligent robotics: Theoretical analysis and systematic application,” Mach. Intell. Res. , vol. 20, no. 1, pp. 1–18, 2023
2023
Closest in time.
S. Zheng, Y. Li, Z. Yu, B. Yu, S.-Y. Cao, M. Wang, J. Xu, R. Ai, W. Gu, L. Luo et al. , “I2p-rec: Recognizing images on large-scale point cloud maps through bird’s eye view projections,” in Proc. Int. Conf. Intell. Robots Syst. IEEE, 2023, pp. 1395–1400
2023
Closest in time.