The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
R $^2$ LIVE: A Robust, Real-Time, LiDAR-Inertial-Visual Tightly-Coupled State Estimator and Mapping
Open accessAbstract
In this letter, we propose a robust, real-time tightly-coupled multi-sensor fusion framework, which fuses measurements from LiDAR, inertial sensor, and visual camera to achieve robust and accurate state estimation. Our proposed framework is composed of two parts: the filter-based odometry and factor graph optimization. To guarantee real-time performance, we estimate the state within the framework of error-state iterated Kalman-filter, and further improve the overall precision with our factor graph optimization. Taking advantage of measurements from all individual sensors, our algorithm is robust enough to various visual failure, LiDAR-degenerated scenarios, and is able to run in real time on an on-board computation platform, as shown by extensive experiments conducted in indoor, outdoor, and mixed environments of different scale (see attached video). Moreover, the results show that our proposed framework can improve the accuracy of state-of-the-art LiDAR-inertial or visual-inertial odometry. To share our findings and to make contributions to the community, we open source our codes on our Github.
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Cites
ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras
Mur-Artal, R., & Tardos, J. D. (2017) · IEEE Transactions on Robotics · doi:10.1109/tro.2017.2705103 · Cited by 6,898
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
LOAM: Lidar Odometry and Mapping in Real-time
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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
LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping
Shan, T., Englot, B., Meyers, D., Wang, W., Ratti, C., & Rus, D. (2020) · 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341176 · Cited by 2,199
Towards fully autonomous driving: Systems and algorithms
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FAST-LIO: A Fast, Robust LiDAR-Inertial Odometry Package by Tightly-Coupled Iterated Kalman Filter
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Self-citationA Tutorial on Quantitative Trajectory Evaluation for Visual(-Inertial) Odometry
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Tightly Coupled 3D Lidar Inertial Odometry and Mapping
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The iterated Kalman filter update as a Gauss-Newton method
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Cited by
FAST-LIO2: Fast Direct LiDAR-Inertial Odometry
Xu, W., Cai, Y., He, D., Lin, J., & Zhang, F. (2022) · IEEE Transactions on Robotics · doi:10.1109/tro.2022.3141876 · Cited by 1,750
Self-citationLin, J., & Zhang, F. (2022) · 2022 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra46639.2022.9811935 · Cited by 423
Self-citationM2DGR: A Multi-Sensor and Multi-Scenario SLAM Dataset for Ground Robots
Yin, J., Li, A., Li, T., Yu, W., & Zou, D. (2022) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3138527 · Cited by 299
Metadata
- Authors
- Lin, J., Zheng, C., Xu, W., & Zhang, F.
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2021-10
- Volume
- 6
- Issue
- 4
- Pages
- 7469-7476
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W3181051035
- Cited by
- 293
- References
- 27
- FWCI
- 24.56
- Type
- journal article
- Language
- en
- Open access
- green
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- Lin, J., Zheng, C., Xu, W., & Zhang, F. (2021). R $^2$ LIVE: A Robust, Real-Time, LiDAR-Inertial-Visual Tightly-Coupled State Estimator and Mapping. IEEE Robotics and Automation Letters. https://doi.org/10.1109/lra.2021.3095515
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