The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Visual-Inertial Monocular SLAM With Map Reuse
Open accessAbstract
In recent years there have been excellent results in visual-inertial odometry techniques, which aim to compute the incremental motion of the sensor with high accuracy and robustness. However, these approaches lack the capability to close loops and trajectory estimation accumulates drift even if the sensor is continually revisiting the same place. In this letter, we present a novel tightly coupled visual-inertial simultaneous localization and mapping system that is able to close loops and reuse its map to achieve zero-drift localization in already mapped areas. While our approach can be applied to any camera configuration, we address here the most general problem of a monocular camera, with its well-known scale ambiguity. We also propose a novel IMU initialization method, which computes the scale, the gravity direction, the velocity, and gyroscope and accelerometer biases, in a few seconds with high accuracy. We test our system in the 11 sequences of a recent micro-aerial vehicle public dataset achieving a typical scale factor error of 1% and centimeter precision. We compare to the state-of-the-art in visual-inertial odometry in sequences with revisiting, proving the better accuracy of our method due to map reuse and no drift accumulation.
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Cites
Multiple View Geometry in Computer Vision
Hartley, R., & Zisserman, A. (2004) · doi:10.1017/cbo9780511811685 · Cited by 21,070
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
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 · cited 1×
“The base of our visual-inertial system is ORB-SLAM [12], [20].”
BackgroundSelf-citationORB-SLAM: A Versatile and Accurate Monocular SLAM System
Mur-Artal, R., Montiel, J. M. M., & Tardos, J. D. (2015) · IEEE Transactions on Robotics · doi:10.1109/tro.2015.2463671 · Cited by 6,778
Self-citationParallel Tracking and Mapping for Small AR Workspaces
Klein, G., & Murray, D. (2007) · 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality · doi:10.1109/ismar.2007.4538852 · Cited by 4,346
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
iSAM2: Incremental smoothing and mapping using the Bayes tree
Kaess, M., Johannsson, H., Roberts, R., Ila, V., Leonard, J. J., & Dellaert, F. (2012) · The International Journal of Robotics Research · doi:10.1177/0278364911430419 · Cited by 1,362
Get Out of My Lab: Large-scale, Real-Time Visual-Inertial Localization
Lynen, S., Sattler, T., Bosse, M., Hesch, J., Pollefeys, M., & Siegwart, R. (2015) · Robotics: Science and Systems XI · doi:10.15607/rss.2015.xi.037 · Cited by 243 · cited 1×
“The recent system [10] is able to reuse a given map, built offline, and perform visual-inertial tracking.”
Background
Cited by
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
“As shown in [22], this sequence can be processed using IMU information.”
MethodSelf-citationVINS-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
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
D3VO: Deep Depth, Deep Pose and Deep Uncertainty for Monocular Visual Odometry
Yang, N., von Stumberg, L., Wang, R., & Cremers, D. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00136 · Cited by 438
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
- Mur-Artal, R., & Tardos, J. D.
- Venue
- IEEE Robotics and Automation Letters
- Published
- 2017-04
- Volume
- 2
- Issue
- 2
- Pages
- 796-803
- ISSN
- 2377-3766 (ISSN-L)
- 2377-3774
- OpenAlex
- W2538522345
- Cited by
- 795
- References
- 8
- FWCI
- 82.98
- Type
- journal article
- Language
- en
- Open access
- green
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