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We propose Super Odometry, a high-precision multi-modal sensor fusion framework, providing a simple but effective way to fuse multiple sensors such as LiDAR, camera, and IMU sensors and achieve robust state estimation in perceptually-degraded environments.
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Cited alongside, same era.
T. Shan, B. Englot, D. Meyers, W. Wang, C. Ratti, and D. Rus, “Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping,” 2020
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S. Zhao, P. Wang, H. Zhang, Z. Fang, and S. Scherer, “Tp-tio: A robust thermal-inertial odometry with deep thermalpoint,” 2020
2020
Cited alongside, same era.
K. Ebadi, Y. Chang, M. Palieri, A. Stephens, A. Hatteland, E. Heiden, A. Thakur, N. Funabiki, B. Morrell, S. Wood, L. Carlone, and A. akbar Agha-mohammadi, “Lamp: Large-scale autonomous mapping and positioning for exploration of perceptually-degraded subterranean environments,” 2020
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2020
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D. Wisth, M. Camurri, S. Das, and M. Fallon, “Unified multi-modal landmark tracking for tightly coupled lidar-visual-inertial odometry,” IEEE Robotics and Automation Letters , vol. 6, no. 2, p. 1004–1011, Apr 2021. [Online]. Available: http://dx.doi.org/10.1109/LRA.2021.3056380
2021
Closest in time.
P. W. Zheng Fang, Shibo Zhao, “Vanishing Point Aided LiDAR-Visual-Inertial Estimator,” in Proc. of the IEEE International Conference on Robotics and Automation (ICRA) , 2021
2021
Closest in time.