The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation
Open accessAbstract
We present LINS, a lightweight lidar-inertial state estimator, for real-time ego-motion estimation. The proposed method enables robust and efficient navigation for ground vehicles in challenging environments, such as feature-less scenes, via fusing a 6-axis IMU and a 3D lidar in a tightly-coupled scheme. An iterated error-state Kalman filter (ESKF) is designed to correct the estimated state recursively by generating new feature correspondences in each iteration, and to keep the system computationally tractable. Moreover, we use a robocentric formulation that represents the state in a moving local frame in order to prevent filter divergence in a long run. To validate robustness and generalizability, extensive experiments are performed in various scenarios. Experimental results indicate that LINS offers comparable performance with the state-of-the-art lidar-inertial odometry in terms of stability and accuracy and has order-of-magnitude improvement in speed.
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Citations
Cites
ROS: an open-source Robot Operating System
Quigley, M. (2009) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · Cited by 8,729
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
Efficient variants of the ICP algorithm
Rusinkiewicz, S., & Levoy, M. (2002) · Proceedings Third International Conference on 3-D Digital Imaging and Modeling · doi:10.1109/im.2001.924423 · Cited by 3,945 · cited 1×
“[6], [7] had surveyed efficient variants of ICP.”
BackgroundLOAM: Lidar Odometry and Mapping in Real-time
Zhang, J., & Singh, S. (2014) · Robotics: Science and Systems X · doi:10.15607/rss.2014.x.007 · Cited by 3,303
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
Real-time loop closure in 2D LIDAR SLAM
Hess, W., Kohler, D., Rapp, H., & Andor, D. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487258 · Cited by 1,938
Barfoot, T. D. (2017) · Cambridge University Press eBooks · doi:10.1017/9781316671528 · Cited by 724
A Review of Point Cloud Registration Algorithms for Mobile Robotics
Pomerleau, F., Colas, F., & Siegwart, R. (2015) · Foundations and Trends® in Robotics · doi:10.1561/2300000035 · Cited by 717
Tightly Coupled 3D Lidar Inertial Odometry and Mapping
Ye, H., Chen, Y., & Liu, M. (2019) · 2019 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2019.8793511 · Cited by 574
Self-citationA robust and modular multi-sensor fusion approach applied to MAV navigation
Lynen, S., Achtelik, M. W., Weiss, S., Chli, M., & Siegwart, R. (2013) · 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems · doi:10.1109/iros.2013.6696917 · Cited by 572
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
Lvi-sam: Tightly-coupled lidar-visual-inertial odometry via smoothing and mapping
Shan, T., Englot, B., Ratti, C., & Rus, D. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9561996 · Cited by 624
M2DGR: 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
R $^2$ LIVE: A Robust, Real-Time, LiDAR-Inertial-Visual Tightly-Coupled State Estimator and Mapping
Lin, J., Zheng, C., Xu, W., & Zhang, F. (2021) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2021.3095515 · Cited by 293
Metadata
- Authors
- Qin, C., Ye, H., Pranata, C. E., Han, J., Zhang, S., & Liu, M.
- Venue
- 2020 IEEE International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2020-05
- Pages
- 8899-8906
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W3091486995
- Cited by
- 391
- References
- 32
- FWCI
- 64.32
- Type
- conference paper
- Language
- en
- Open access
- green
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- Qin, C., Ye, H., Pranata, C. E., Han, J., Zhang, S., & Liu, M. (2020). LINS: A Lidar-Inertial State Estimator for Robust and Efficient Navigation. 2020 IEEE International Conference on Robotics and Automation (ICRA). https://doi.org/10.1109/icra40945.2020.9197567
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