The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
FAST-LIO: A Fast, Robust LiDAR-Inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter
Abstract
This letter presents a computationally efficient and robust LiDAR-inertial odometry framework. We fuse LiDAR feature points with IMU data using a tightly-coupled iterated extended Kalman filter to allow robust navigation in fast-motion, noisy or cluttered environments where degeneration occurs. To lower the computation load in the presence of a large number of measurements, we present a new formula to compute the Kalman gain. The new formula has computation load depending on the state dimension instead of the measurement dimension. The proposed method and its implementation are tested in various indoor and outdoor environments. In all tests, our method produces reliable navigation results in real-time: running on a quadrotor onboard computer, it fuses more than 1200 effective feature points in a scan and completes all iterations of an iEKF step within 25 ms. Our codes are open-sourced on Github.1
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Citations
Cites
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator
Qin, T., Li, P., & Shen, S. (2018) · IEEE Transactions on Robotics · doi:10.1109/tro.2018.2853729 · Cited by 4,669
Accuracy and Stability of Numerical Algorithms: Second Edition
Higham, N. J. (2002) · doi:10.1137/1.9780898718027 · Cited by 3,803
Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain
Shan, T., & Englot, B. (2018) · 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2018.8594299 · Cited by 2,295
On-Manifold Preintegration for Real-Time Visual--Inertial Odometry
Forster, C., Carlone, L., Dellaert, F., & Scaramuzza, D. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2016.2597321 · Cited by 1,502
Iterated extended Kalman filter based visual-inertial odometry using direct photometric feedback
Bloesch, M., Burri, M., Omari, S., Hutter, M., & Siegwart, R. (2017) · The International Journal of Robotics Research · doi:10.1177/0278364917728574 · Cited by 485
Lic-fusion: Lidar-inertial-camera odometry
Zuo, X., Geneva, P., Lee, W., Liu, Y., & Huang, G. (2019) · 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8967746 · Cited by 222
Metadata
- Authors
- Xu, W., & Zhang, F.
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2021-04
- Volume
- 6
- Issue
- 2
- Pages
- 3317-3324
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W3133648073
- Cited by
- 1,127
- References
- 6
- FWCI
- 67.49
- Type
- journal article
- Language
- en
- Open access
- closed
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- Xu, W., & Zhang, F. (2021). FAST-LIO: A Fast, Robust LiDAR-Inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter. IEEE Robotics and Automation Letters. https://doi.org/10.1109/lra.2021.3064227
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