The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Visual-Inertial Sensor Fusion: Localization, Mapping and Sensor-to-Sensor Self-calibration
Abstract
Visual and inertial sensors, in combination, are able to provide accurate motion estimates and are well suited for use in many robot navigation tasks. However, correct data fusion, and hence overall performance, depends on careful calibration of the rigid body transform between the sensors. Obtaining this calibration information is typically difficult and time-consuming, and normally requires additional equipment. In this paper we describe an algorithm, based on the unscented Kalman filter, for self-calibration of the transform between a camera and an inertial measurement unit (IMU). Our formulation rests on a differential geometric analysis of the observability of the camera—IMU system; this analysis shows that the sensor-to-sensor transform, the IMU gyroscope and accelerometer biases, the local gravity vector, and the metric scene structure can be recovered from camera and IMU measurements alone. While calibrating the transform we simultaneously localize the IMU and build a map of the surroundings, all without additional hardware or prior knowledge about the environment in which a robot is operating. We present results from simulation studies and from experiments with a monocular camera and a low-cost IMU, which demonstrate accurate estimation of both the calibration parameters and the local scene structure.
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Citations
Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Parallel 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
MonoSLAM: Real-time single camera SLAM
Davison, A. J., Reid, I. D., Molton, N. D., & Stasse, O. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1049 · Cited by 3,959
Nonlinear controllability and observability
Hermann, R., & Krener, A. (1977) · IEEE Transactions on Automatic Control · doi:10.1109/tac.1977.1101601 · Cited by 2,374 · cited 5×
“if, for each point x0 ∈ M , there exists an open neighborhood U ⊆ M such that for every open neighborhood V of x0, V ⊆ U , there is no other state x1 ∈ V that is indistinguishable from x0 (Hermann and Krener 1977).”
BackgroundMethodInfluentialVision-Aided Inertial Navigation for Spacecraft Entry, Descent, and Landing
Mourikis, A. I., Trawny, N., Roumeliotis, S. I., Johnson, A. E., Ansar, A., & Matthies, L. (2009) · IEEE Transactions on Robotics · doi:10.1109/tro.2009.2012342 · Cited by 345 · cited 1×
“A variety of hybrid visual–inertial systems have been developed, for applications from short-range sensor localization (Strelow and Singh 2002) to long-range motion estimation for spacecraft entry, descent and landing (Mourikis et al. 2009).”
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Cited by
Robust visual inertial odometry using a direct EKF-based approach
Bloesch, M., Omari, S., Hutter, M., & Siegwart, R. (2015) · 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2015.7353389 · Cited by 917
High-precision, consistent EKF-based visual-inertial odometry
Li, M., & Mourikis, A. I. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913481251 · Cited by 813
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
Weiss, S., Achtelik, M. W., Lynen, S., Chli, M., & Siegwart, R. (2012) · 2012 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2012.6225147 · Cited by 417
A UAV system for inspection of industrial facilities
Nikolic, J., Burri, M., Rehder, J., Leutenegger, S., Huerzeler, C., & Siegwart, R. (2013) · 2013 IEEE Aerospace Conference · doi:10.1109/aero.2013.6496959 · Cited by 251
Metadata
- Authors
- Kelly, J., & Sukhatme, G. S.
- Venue
- The International Journal of Robotics Research
- Publisher
- SAGE Publications
- Published
- 2010-11-05
- Volume
- 30
- Issue
- 1
- Pages
- 56-79
- ISSN
- 0278-3649 (ISSN-L)
- 1741-3176
- OpenAlex
- W2147536420
- Cited by
- 536
- References
- 6
- FWCI
- 51.34
- Type
- journal article
- Language
- en
- Open access
- closed
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