The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Tightly Coupled 3D Lidar Inertial Odometry and Mapping
Open accessAbstract
Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the cost derived from lidar and IMU measurements, the lidarIMU odometry (LIO) can perform well with considerable drifts after long-term experiment, even in challenging cases where the lidar measurement can be degraded. Besides, to obtain more reliable estimations of the lidar poses, a rotation-constrained refinement algorithm (LIO-mapping) is proposed to further align the lidar poses with the global map. The experiment results demonstrate that the proposed method can estimate the poses of the sensor pair at the IMU update rate with high precision, even under fast motion conditions or with insufficient features.
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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
LOAM: 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
Least-squares estimation of transformation parameters between two point patterns
Umeyama, S. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.88573 · Cited by 2,420
Keyframe-based visual–inertial odometry using nonlinear optimization
Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R., & Furgale, P. (2015) · The International Journal of Robotics Research · doi:10.1177/0278364914554813 · Cited by 1,723
Visual-Inertial Monocular SLAM With Map Reuse
Mur-Artal, R., & Tardos, J. D. (2017) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2017.2653359 · Cited by 795
A 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
Efficient Surfel-Based SLAM using 3D Laser Range Data in Urban Environments
Behley, J., & Stachniss, C. (2018) · Robotics: Science and Systems XIV · doi:10.15607/rss.2018.xiv.016 · Cited by 494
Visual-inertial navigation, mapping and localization: A scalable real-time causal approach
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Continuous 3D scan-matching with a spinning 2D laser
Bosse, M., & Zlot, R. (2009) · 2009 IEEE International Conference on Robotics and Automation · doi:10.1109/robot.2009.5152851 · Cited by 289
LiDAR Scan Matching Aided Inertial Navigation System in GNSS-Denied Environments
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Self-citation
Cited by
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
Lic-fusion 2.0: Lidar-inertial-camera odometry with sliding-window plane-feature tracking
Zuo, X., Yang, Y., Geneva, P., Lv, J., Liu, Y., Huang, G., & Pollefeys, M. (2020) · 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9340704 · Cited by 168
“We appreciate the help from the authors of LIOMAP [22] for parameters tuning to achieve better accuracy.”
MethodFC-SLAM: Federated Learning Enhanced Distributed Visual-LiDAR SLAM In Cloud Robotic System
Li, Z., Wang, L., Jiang, L., & Xu, C.-Z. (2019) · 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO) · doi:10.1109/robio49542.2019.8961798 · Cited by 28
Metadata
- Authors
- Ye, H., Chen, Y., & Liu, M.
- Venue
- 2019 International Conference on Robotics and Automation (ICRA)
- Publisher
- IEEE
- Published
- 2019-05
- Pages
- 3144-3150
- ISSN
- 1050-4729 (ISSN-L)
- OpenAlex
- W2938818630
- Cited by
- 574
- References
- 15
- FWCI
- 102.43
- Type
- conference paper
- Language
- en
- Open access
- green
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