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LiDAR odometry and localization are two widely used and fundamental applications in robotic and autonomous driving systems.
P. J. Besl and N. D. McKay, “Method for registration of 3-D shapes,” in Sensor Fusion IV: Control Paradigms and Data Structures , P. S. Schenker, Ed., vol. 1611, International Society for Optics and Photonics. SPIE, 1992, pp. 586 – 606
1992
Earlier work this paper cites.
P. Biber and W. Strasser, “The normal distributions transform: a new approach to laser scan matching,” in Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453) , vol. 3, 2003, pp. 2743–2748 vol.3
2003
Earlier work this paper cites.
P. Newman and K. Ho, “Slam-loop closing with visually salient features,” in proceedings of the 2005 IEEE International Conference on Robotics and Automation . IEEE, 2005, pp. 635–642
2005
Earlier work this paper cites.
R. Kümmerle, B. Steder, C. Dornhege, M. Ruhnke, G. Grisetti, C. Stachniss, and A. Kleiner, “On measuring the accuracy of slam algorithms,” Autonomous Robots , vol. 27, pp. 387–407, 2009
2009
Earlier work this paper cites.
R. H. Rasshofer, M. Spies, and H. Spies, “Influences of weather phenomena on automotive laser radar systems,” Advances in Radio Science , vol. 9, pp. 49–60, 2011
2011
Earlier work this paper cites.
M. Hongchao and W. Jianwei, “Analysis of positioning errors caused by platform vibration of airborne lidar system,” 2012 8th IEEE International Symposium on Instrumentation and Control Technology (ISICT) Proceedings , pp. 257–261, 2012
2012
Earlier work this paper cites.
L. Mona, Z. Liu, D. Müller, A. Omar, A. Papayannis, G. Pappalardo, N. Sugimoto, and M. Vaughan, “Lidar measurements for desert dust characterization: An overview,” Advances in Meteorology , vol. 2012, no. 1, p. 356265, 2012
2012
Earlier work this paper cites.
H. Ma and J. Wu, “Analysis of positioning errors caused by platform vibration of airborne lidar system,” in 2012 8th IEEE International Symposium on Instrumentation and Control Technology (ISICT) Proceedings , 2012, pp. 257–261
2012
Earlier work this paper cites.
A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the kitti vision benchmark suite,” in Conference on Computer Vision and Pattern Recognition (CVPR) , 2012
2012
Earlier work this paper cites.
J. Zhang, S. Singh et al. , “Loam: Lidar odometry and mapping in real-time.” in Robotics: Science and systems , vol. 2, no. 9. Berkeley, CA, 2014, pp. 1–9
2014
Earlier work this paper cites.
K. Yoneda, H. Tehrani, T. Ogawa, N. Hukuyama, and S. Mita, “Lidar scan feature for localization with highly precise 3-d map,” in 2014 IEEE Intelligent Vehicles Symposium Proceedings , 2014, pp. 1345–1350
2014
Earlier work this paper cites.
I. Bukhori and Z. H. Ismail, “Detection of kidnapped robot problem in monte carlo localization based on the natural displacement of the robot,” International Journal of Advanced Robotic Systems , vol. 14, 2017
2017
Earlier work this paper cites.
W. Maddern, G. Pascoe, C. Linegar, and P. Newman, “1 Year, 1000km: The Oxford RobotCar Dataset,” The International Journal of Robotics Research (IJRR) , vol. 36, no. 1, pp. 3–15, 2017
2017
Earlier work this paper cites.
J. Digne and C. de Franchis, “The Bilateral Filter for Point Clouds,” Image Processing On Line , vol. 7, pp. 278–287, 2017
2017
Earlier work this paper cites.
G. Wan, X. Yang, R. Cai, H. Li, Y. Zhou, H. Wang, and S. Song, “Robust and precise vehicle localization based on multi-sensor fusion in diverse city scenes,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) , 2018, pp. 4670–4677
2018
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 2019 IEEE/CVF International Conference on Computer Vision (ICCV) , 2019, pp. 12–21
2019
Cited alongside, same era.
W. Lu, Y. Zhou, G. Wan, S. Hou, and S. Song, “L3-net: Towards learning based lidar localization for autonomous driving,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 6389–6398
2019
Cited alongside, same era.
Y. Cao, C. Xiao, B. Cyr, Y. Zhou, W. Park, S. Rampazzi, Q. A. Chen, K. Fu, and Z. M. Mao, “Adversarial sensor attack on LiDAR-based perception in autonomous driving,” in Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security . ACM, nov 2019
L. Zhou, G. Sun, Y. Li, W. Li, and Z. Su, “Point cloud denoising review: from classical to deep learning-based approaches,” Graphical Models , vol. 121, p. 101140, 2022
2022
Later among the works it cites.
Q. Xu, Y. Zhong, and U. Neumann, “Behind the curtain: Learning occluded shapes for 3d object detection,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 3, 2022, pp. 2893–2901
2022
Later among the works it cites.
J. Laconte, D. Lisus, and T. D. Barfoot, “Toward certifying maps for safe registration-based localization under adverse conditions,” IEEE Robotics and Automation Letters , vol. 9, no. 2, pp. 1572–1579, 2023
2023
Later among the works it cites.
L. Wiesmann, T. Guadagnino, I. Vizzo, N. Zimmerman, Y. Pan, H. Kuang, J. Behley, and C. Stachniss, “Locndf: Neural distance field mapping for robot localization,” IEEE Robotics and Automation Letters , vol. 8, no. 8, pp. 4999–5006, 2023
2023
Later among the works it cites.
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2019
Cited alongside, same era.
J. Shen, J. Y. Won, Z. Chen, and Q. A. Chen, “Drift with devil: Security of Multi-Sensor fusion based localization in High-Level autonomous driving under GPS spoofing,” in 29th USENIX Security Symposium (USENIX Security 20) . USENIX Association, Aug. 2020, pp. 931–948
2020
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,” in ECCV , 2020
2020
Cited alongside, same era.
J. Nubert, S. Khattak, and M. Hutter, “Self-supervised learning of lidar odometry for robotic applications,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 9601–9607
2021
Cited alongside, same era.
Y. Pan, P. Xiao, Y. He, Z. Shao, and Z. Li, “Mulls: Versatile lidar slam via multi-metric linear least square,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021, pp. 11 633–11 640
2021
Cited alongside, same era.
2021
Cited alongside, same era.
M. Hahner, C. Sakaridis, D. Dai, and L. Van Gool, “Fog Simulation on Real LiDAR Point Clouds for 3D Object Detection in Adverse Weather,” in IEEE International Conference on Computer Vision (ICCV) , 2021
2021
Cited alongside, same era.
I. Vizzo, T. Guadagnino, B. Mersch, L. Wiesmann, J. Behley, and C. Stachniss, “Kiss-icp: In defense of point-to-point icp – simple, accurate, and robust registration if done the right way,” IEEE Robotics and Automation Letters , vol. 8, pp. 1029–1036, 2022
2022
Cited alongside, same era.
K. Yoshida, M. Hojo, and T. Fujino, “Adversarial scan attack against scan matching algorithm for pose estimation in lidar-based slam,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences , vol. 105, no. 3, pp. 326–335, 2022
2022
Cited alongside, same era.
J. Deng, Q. Wu, X. Chen, S. Xia, Z. Sun, G. Liu, W. Yu, and L. Pei, “Nerf-loam: Neural implicit representation for large-scale incremental lidar odometry and mapping,” in Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) , October 2023, pp. 8218–8227
2023
Later among the works it cites.
D. Hegde, V. Kilic, V. Sindagi, A. B. Cooper, M. Foster, and V. M. Patel, “Source-free unsupervised domain adaptation for 3d object detection in adverse weather,” in 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2023, pp. 6973–6980
2023
Later among the works it cites.
S. Li, Z. Wang, F. Juefei-Xu, Q. Guo, X. Li, and L. Ma, “Common corruption robustness of point cloud detectors: Benchmark and enhancement,” IEEE Transactions on Multimedia , pp. 1–12, 2023
2023
Later among the works it cites.
Y. Dong, C. Kang, J. Zhang, Z. Zhu, Y. Wang, X. Yang, H. Su, X. Wei, and J. Zhu, “Benchmarking robustness of 3d object detection to common corruptions,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2023, pp. 1022–1032
2023
Later among the works it cites.
S. Uttarkabat, S. Appukuttan, K. Gupta, S. Nayak, and P. Palo, “Bloomnet: Perception of blooming effect in adas using synthetic lidar point cloud data,” in 2024 IEEE Intelligent Vehicles Symposium (IV) , 2024, pp. 1886–1892
2024
Closest in time.
Y. Zhang, P. Shi, and J. Li, “3d lidar slam: A survey,” The Photogrammetric Record , vol. 39, pp. 457 – 517, 2024
2024
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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
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S. Yi, J. Gao, Y. Lyu, L. Hua, X. Liang, and Q. Pan, “A 3d point attacker for lidar-based localization,” in 2024 IEEE 18th International Conference on Control & Automation (ICCA) , 2024, pp. 685–691
2024
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W. Liao, S. Yan, Y. Zhang, X. Zhai, Y. Wang, and E. Fu, “Is your autonomous vehicle safe? understanding the threat of electromagnetic signal injection attacks on traffic scene perception,” Proceedings of the AAAI Conference on Artificial Intelligence , vol. 39, no. 26, pp. 27 464–27 472, Apr. 2025
2025
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Z. Zhang, J. Laconte, D. Lisus, and T. D. Barfoot, “Prepared for the worst: Resilience analysis of the icp algorithm via learning-based worst-case adversarial attacks,” in 2025 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2025, pp. 15 174–15 180
2025
Closest in time.