Online Multi-IMU Calibration Using Visual-Inertial Odometry (2310.12411v2)
Abstract: This work presents a centralized multi-IMU filter framework with online intrinsic and extrinsic calibration for unsynchronized inertial measurement units that is robust against changes in calibration parameters. The novel EKF-based method estimates the positional and rotational offsets of the system of sensors as well as their intrinsic biases without the use of rigid body geometric constraints. Additionally, the filter is flexible in the total number of sensors used while leveraging the commonly used MSCKF framework for camera measurements. The filter framework has been validated using Monte Carlo simulation as well as experimentally. In both simulations and experiments, using multiple IMU measurement streams within the proposed filter framework outperforms the use of a single IMU in a filter prediction step while also producing consistent and accurate estimates of initial calibration errors. Compared to current state-of-the-art optimizers, the filter produces similar intrinsic and extrinsic calibration parameters for each sensor. Finally, an open source repository has been provided at https://github.com/unmannedlab/ekf-cal containing both the online estimator and the simulation used for testing and evaluation.
- M. Faizullin and G. Ferrer, “Best axes composition: Multiple gyroscopes imu sensor fusion to reduce systematic error,” in 2021 European Conference on Mobile Robots (ECMR), 2021, pp. 1–7.
- U. N. Patel and I. A. Faruque, “Multi-imu based alternate navigation frameworks: Performance and comparison for uas,” IEEE Access, vol. 10, pp. 17 565–17 577, 2022.
- J. B. Bancroft and G. Lachapelle, “Data fusion algorithms for multiple inertial measurement units,” Sensors, vol. 11, no. 7, pp. 6771–6798, 2011. [Online]. Available: https://www.mdpi.com/1424-8220/11/7/6771
- M. Zhang, X. Xu, Y. Chen, and M. Li, “A lightweight and accurate localization algorithm using multiple inertial measurement units,” IEEE Robotics and Automation Letters, vol. 5, no. 2, pp. 1508–1515, 2020.
- E. D’Amato, M. Mattei, A. Mele, I. Notaro, and V. Scordamaglia, “Fault tolerant low cost imus for uavs,” in 2017 IEEE International Workshop on Measurement and Networking (M&N), 2017, pp. 1–6.
- K. Eckenhoff, P. Geneva, and G. Huang, “Sensor-failure-resilient multi-imu visual-inertial navigation,” in 2019 International Conference on Robotics and Automation (ICRA), 2019, pp. 3542–3548.
- H. Huang, H. Zhang, and L. Jiang, “An optimal fusion method of multiple inertial measurement units based on measurement noise variance estimation,” IEEE Sensors Journal, vol. 23, no. 3, pp. 2693–2706, 2023.
- A. Larey, E. Aknin, and I. Klein, “Multiple inertial measurement units-an empirical study,” IEEE Access, vol. 8, pp. 75 656–75 665, 2020.
- N. Carlson, “Federated filter for fault-tolerant integrated navigation systems,” in IEEE PLANS ’88.,Position Location and Navigation Symposium, Record. ’Navigation into the 21st Century’., 1988, pp. 110–119.
- Q. Luo, X. Yan, Z. Zhou, C. Wang, and C. Hu, “An integrated navigation and localization system,” in 2021 IEEE International Conference on Smart Internet of Things (SmartIoT), 2021, pp. 28–32.
- L. Wang, H. Tang, T. Zhang, Q. Chen, J. Shi, and X. Niu, “Improving the navigation performance of the mems imu array by precise calibration,” IEEE Sensors Journal, vol. 21, no. 22, pp. 26 050–26 058, 2021.
- J. Rehder, J. Nikolic, T. Schneider, T. Hinzmann, and R. Siegwart, “Extending kalibr: Calibrating the extrinsics of multiple imus and of individual axes,” in 2016 IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 4304–4311.
- J. B. Bancroft, “Multiple imu integration for vehicular navigation,” in Proceedings of the 22nd International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS 2009), 2009, pp. 1828–1840.
- K. Eckenhoff, P. Geneva, and G. Huang, “MIMC-VINS: A versatile and resilient multi-imu multi-camera visual-inertial navigation system,” IEEE Transactions on Robotics, vol. 37, no. 5, pp. 1360–1380, 2021.
- A. I. Mourikis and S. I. Roumeliotis, “A multi-state constraint kalman filter for vision-aided inertial navigation,” in Proceedings 2007 IEEE International Conference on Robotics and Automation, 2007, pp. 3565–3572.
- M. Bloesch, H. Sommer, T. Laidlow, M. Burri, G. Nützi, P. Fankhauser, D. Bellicoso, C. Gehring, S. Leutenegger, M. Hutter, and R. Y. Siegwart, “A primer on the differential calculus of 3d orientations,” ArXiv, vol. abs/1606.05285, 2016. [Online]. Available: https://api.semanticscholar.org/CorpusID:2141
- J. Hartzer, “ekf-cal: Extended kalman filter calibration and localization,” 2023. [Online]. Available: https://github.com/unmannedlab/ekf-cal
- D. Prokhorov, D. Zhukov, O. Barinova, K. Anton, and A. Vorontsova, “Measuring robustness of visual slam,” in 2019 16th International Conference on Machine Vision Applications (MVA), 2019, pp. 1–6.