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Inertial odometry (IO) using strap-down inertial measurement units (IMUs) is critical in many robotic applications where precise orientation and position tracking are essential.
J. Hung, J. Hunter, W. Stripling, and H. White, “Size effect on navigation using a strapdown imu,”
1979
Earlier work this paper cites.
B. Barshan and H. F. Durrant-Whyte, “Inertial navigation systems for mobile robots,”
1995
Earlier work this paper cites.
H. Qi and J. B. Moore, “Direct kalman filtering approach for gps/ins integration,”
2002
Earlier work this paper cites.
N. El-Sheimy, H. Hou, and X. Niu, “Analysis and modeling of inertial sensors using allan variance,”
2007
Earlier work this paper cites.
C. Krebs, “Generic imu-camera calibration algorithm: Influence of imu-axis on each other,” Autonomous Systems Lab, ETH Zurich,” Tech. Rep., 2012, [Online]. Available:
2012
Earlier work this paper cites.
A. Geiger, P. Lenz, C. Stiller, and R. Urtasun, “Vision meets robotics: the kitti dataset,”
2013
Earlier work this paper cites.
P. Furgale, J. Rehder, and R. Siegwart, “Unified temporal and spatial calibration for multi-sensor systems,” in
2013
Earlier work this paper cites.
X. Niu, Y. Li, H. Zhang, Q. Wang, and Y. Ban, “Fast thermal calibration of low-grade inertial sensors and inertial measurement units,”
2013
Earlier work this paper cites.
M. Li and A. I. Mourikis, “Online temporal calibration for camera–imu systems: Theory and algorithms,”
2014
Earlier work this paper cites.
D. Tedaldi, A. Pretto, and E. Menegatti, “A robust and easy to implement method for imu calibration without external equipments,” in
2014
Earlier work this paper cites.
J. D. Hincapié-Ramos, K. Ozacar, P. P. Irani, and Y. Kitamura, “Gyrowand: Imu-based raycasting for augmented reality head-mounted displays,” in
2015
Earlier work this paper cites.
C. Forster, L. Carlone, F. Dellaert, and D. Scaramuzza, “Imu preintegration on manifold for efficient visual-inertial maximum-a-posteriori estimation,” 2015
2015
Earlier work this paper cites.
M. Burri, J. Nikolic, P. Gohl, T. Schneider, J. Rehder, S. Omari, M. W. Achtelik, and R. Siegwart, “The euroc micro aerial vehicle datasets,”
2016
Earlier work this paper cites.
J. Rehder, J. Nikolic, T. Schneider, T. Hinzmann, and R. Siegwart, “Extending kalibr: Calibrating the extrinsics of multiple imus and of individual axes,” in
2016
Earlier work this paper cites.
G. Hemann, S. Singh, and M. Kaess, “Long-range gps-denied aerial inertial navigation with lidar localization,” in
2016
Earlier work this paper cites.
J. Rehder and R. Siegwart, “Camera/imu calibration revisited,”
2017
Cited alongside, same era.
U. Qureshi and F. Golnaraghi, “An algorithm for the in-field calibration of a mems imu,”
2017
Cited alongside, same era.
T. Qin, P. Li, and S. Shen, “Vins-mono: A robust and versatile monocular visual-inertial state estimator,”
2018
Cited alongside, same era.
C. Chen, X. Lu, A. Markham, and N. Trigoni, “Ionet: Learning to cure the curse of drift in inertial odometry,” in
2018
Cited alongside, same era.
H. Yan, Q. Shan, and Y. Furukawa, “Ridi: Robust imu double integration,” in
2018
Cited alongside, same era.
D. Schubert, T. Goll, N. Demmel, V. Usenko, J. Stückler, and D. Cremers, “The tum vi benchmark for evaluating visual-inertial odometry,” in
S. Sun, D. Melamed, and K. Kitani, “Idol: Inertial deep orientation-estimation and localization,” in
2021
Later among the works it cites.
M. Zhang, M. Zhang, Y. Chen, and M. Li, “Imu data processing for inertial aided navigation: A recurrent neural network based approach,” in
2021
Later among the works it cites.
M. Brossard, A. Barrau, P. Chauchat, and S. Bonnabel, “Associating uncertainty to extended poses for on lie group imu preintegration with rotating earth,”
2021
Later among the works it cites.
R. L. Russell and C. Reale, “Multivariate uncertainty in deep learning,”
2021
Later among the works it cites.
R. Li, C. Fu, W. Yi, and X. Yi, “Calib-net: Calibrating the low-cost imu via deep convolutional neural network,”
2022
Later among the works it cites.
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2018
Cited alongside, same era.
F. Nobre and C. Heckman, “Learning to calibrate: Reinforcement learning for guided calibration of visual–inertial rigs,”
2019
Cited alongside, same era.
M. Menolotto, D.-S. Komaris, S. Tedesco, B. O’Flynn, and M. Walsh, “Motion capture technology in industrial applications: A systematic review,”
2020
Cited alongside, same era.
D. Capriglione, M. Carratù, M. Catelani, L. Ciani, G. Patrizi, A. Pietrosanto, and P. Sommella, “Experimental analysis of filtering algorithms for imu-based applications under vibrations,”
2020
Cited alongside, same era.
S. Herath, H. Yan, and Y. Furukawa, “Ronin: Robust neural inertial navigation in the wild: Benchmark, evaluations, & new methods,” in
2020
Cited alongside, same era.
M. Brossard, A. Barrau, and S. Bonnabel, “Ai-imu dead-reckoning,”
2020
Cited alongside, same era.
W. Liu, D. Caruso, E. Ilg, J. Dong, A. I. Mourikis, K. Daniilidis, V. Kumar, and J. Engel, “Tlio: Tight learned inertial odometry,”
2020
Cited alongside, same era.
R. Buchanan, V. Agrawal, M. Camurri, F. Dellaert, and M. Fallon, “Deep imu bias inference for robust visual-inertial odometry with factor graphs,”
2022
Later among the works it cites.
R. Buchanan, M. Camurri, F. Dellaert, and M. Fallon, “Learning inertial odometry for dynamic legged robot state estimation,” in
2022
Later among the works it cites.
2022
Later among the works it cites.
X. Cao, C. Zhou, D. Zeng, and Y. Wang, “Rio: Rotation-equivariance supervised learning of robust inertial odometry,” in
2022
Later among the works it cites.
S. Zhao, D. Singh, H. Sun, R. Jiang, Y. Gao, T. Wu, J. Karhade, C. Whittaker, I. Higgins, J. Xu,
2023
Closest in time.
C.-S. Jao, D. Wang, and A. M. Shkel, “Prio-imu: Prioritizable imu array for enhancing foot-mounted inertial navigation accuracy,” in
2023
Closest in time.
C. Wang, D. Gao, K. Xu, J. Geng, Y. Hu, Y. Qiu, B. Li, F. Yang, B. Moon, A. Pandey,
2023
Closest in time.
Y. Yang, P. Geneva, X. Zuo, and G. Huang, “Online self-calibration for visual-inertial navigation: Models, analysis, and degeneracy,”
2023
Closest in time.