Abstract
We present a new approach to assisted intrinsic and extrinsic calibration with an observability-aware visual-inertial calibration system that guides the user through the calibration procedure by suggesting easy-to-perform motions that render the calibration parameters observable. This is done by identifying which subset of the parameter space is rendered observable with a rank-revealing decomposition of the Fisher information matrix, modeling calibration as a Markov decision process and using reinforcement learning to establish which discrete sequence of motions optimizes for the regression of the desired parameters. The goal is to address the assumption common to most calibration solutions: that sufficiently informative motions are provided by the operator. We do not make use of a process model and instead leverage an experience based approach that is broadly applicable to any platform. This is a step in the direction of long term autonomy and “power-on-and-go” robotic systems, making repeatable and reliable calibration accessible to the non-expert operator.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The details of this implementation are omitted due to space limitations.
References
Agarwal, S., Mierle, K.: Others: ceres solver. http://ceres-solver.org
Autonomous Robotics and Perception Group (ARPG): VICalib visual-inertial calibration suite. https://github.com/arpg/vicalib (2016)
Brookshire, J., Teller, S.: Extrinsic calibration from per-sensor egomotion. Robot.: Sci. Syst. VIII 5, 504–512 (2013)
Durrant-Whyte, H., Bailey, T.: Simultaneous localization and mapping: part I. IEEE Robot. Autom. Mag. 13(2), 99–110 (2006)
François-Lavet, V., Fonteneau, R., Ernst, D.: How to discount deep reinforcement learning: towards new dynamic strategies. arXiv:1512.02011 (2015)
Gibbs, B.P.: Advanced Kalman Filtering, Least-squares and Modeling: A Practical Handbook. Wiley, New York (2011)
Golub, G.H., Van Loan, C.F.: Matrix Computations, vol. 3. JHU Press (2012)
Hansen, P.C.: Rank-Deficient and Discrete Ill-Posed Problems: Numerical Aspects of Linear Inversion. SIAM (1998)
Hausman, K., Preiss, J., Sukhatme, G.S., Weiss, S.: Observability-Aware Trajectory Optimization for Self-Calibration with Application to UAVs. arxiv.org (2016)
Hermann, R., Krener, A.: Nonlinear controllability and observability. IEEE Trans. Autom. Control 22(5), 728–740 (1977)
Jauffret, C.: Observability and fisher information matrix in nonlinear regression. IEEE Trans. Aerosp. Electron. Syst. 43(2), 756–759 (2007)
Keivan, N., Sibley, G.: Constant-time monocular self-calibration. In: Robotics and Biomimetics (ROBIO), pp. 1590–1595 (2014)
Kelly, J., Sukhatme, G.S.: Visual-inertial sensor fusion: localization, mapping and sensor-to-sensor self-calibration. Int. J. Robot. Res. 30(1), 56–79 (2011)
Kümmerle, R., Grisetti, G., Burgard, W.: Simultaneous calibration, localization, and mapping. In: 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 3716–3721. IEEE (2011)
Leutenegger, S., Chli, M., Siegwart, R.Y.: BRISK: binary robust invariant scalable keypoints. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 2548–2555. IEEE (2011)
Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R., Furgale, P.: Keyframe-based visual-inertial odometry using nonlinear optimization. Int. J. Robot. Res. 34(3), 314–334 (2015)
Levinson, J., Thrun, S.: Unsupervised calibration for multi-beam lasers. In: Experimental Robotics, pp. 179–193. Springer, Berlin (2014)
Low, K.H.: Industrial robotics: programming, simulation and applications. I-Tech (2007)
Martinelli, A., Scaramuzza, D., Siegwart, R.: Automatic self-calibration of a vision system during robot motion. In: Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006, pp. 43–48. IEEE (2006)
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A.A., Veness, J., Bellemare, M.G., Graves, A., Riedmiller, M., Fidjeland, A.K., Ostrovski, G., et al.: Human-level control through deep reinforcement learning. Nature 518(7540), 529–533 (2015)
Nobre, F., Heckman, C., Sibley, G.: Multi-sensor slam with online self-calibration and change detection. In: International Symposium on Experimental Robotics (ISER) (2016)
Richardson, A., Strom, J., Olson, E.: Aprilcal: Assisted and repeatable camera calibration. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1814–1821. IEEE (2013)
Sheehan, M., Harrison, A., Newman, P.: Self-calibration for a 3d laser. Int. J. Robot. Res. 31(5), 675–687 (2012)
Strasdat, H., Montiel, J., Davison, A.J.: Real-time monocular slam: why filter? In: 2010 IEEE International Conference on Robotics and Automation (ICRA), pp. 2657–2664. IEEE (2010)
Sturm, P.F., Maybank, S.J.: On plane-based camera calibration: a general algorithm, singularities, applications. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999, vol. 1, pp. 432–437. IEEE (1999)
Zhang, Q., Pless, R.: Extrinsic calibration of a camera and laser range finder (improves camera calibration). In: Proceedings. 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2004. (IROS 2004), vol. 3, pp. 2301–2306. IEEE (2004)
Acknowledgements
This work was supported by DARPA DSO “Ninja Car” award no. N65236-16–1–1000.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Nobre, F., Heckman, C. (2020). Reinforcement Learning for Assisted Visual-Inertial Robotic Calibration. In: Amato, N., Hager, G., Thomas, S., Torres-Torriti, M. (eds) Robotics Research. Springer Proceedings in Advanced Robotics, vol 10. Springer, Cham. https://doi.org/10.1007/978-3-030-28619-4_36
Download citation
DOI: https://doi.org/10.1007/978-3-030-28619-4_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-28618-7
Online ISBN: 978-3-030-28619-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)