Abstract
Visual-inertial navigation that is able to provide accurate 3D localization in GPS-denied environments has seen popularity in recent years due to the proliferation of cost-effective cameras and inertial measurement units (IMUs). While an extended Kalman filter (EKF) is often used for sensor fusion, factor graph-based optimization has recently revealed its superior performance, which, however, is still compromised by the lack of rigorous IMU preintegration (i.e., integrating IMU measurements in a local frame of reference). To address this issue, in this paper, we analytically derive preintegration based on closed-form solutions of the continuous IMU measurement equations. These expressions allow us to analytically compute the mean, covariance, and bias Jacobians for a set of IMU preintegration factors. These accurate factors are subsequently fused with the visual information via visual-inertial factor graph optimization to provide high-precision trajectory estimates. The proposed method is validated on both Monte Carlo simulations and real-world experiments.
Guoquan Huang: This work was partially supported by the University of Delaware College of Engineering, UD Cybersecurity Initiative, the Delaware NASA/EPSCoR Seed Grant, the NSF (IIS-1566129), and the DTRA (HDTRA1-16-1-0039).
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References
Hesch, J., Kottas, D., Bowman, S., Roumeliotis, S.: Camera-IMU-based localization: Observability analysis and consistency improvement. International Journal of Robotics Research 33 (2014) 182–201
Li, M., Mourikis, A.: High-precision, consistent EKF-based visual-inertial odometry. International Journal of Robotics Research 32(6) (2013) 690–711
Huang, G., Kaess, M., Leonard, J.: Towards consistent visual-inertial navigation. In: Proc. of the IEEE International Conference on Robotics and Automation, Hong Kong, China (May 31–June 7, 2014) 4926–4933
Kumar, V., Michael, N.: Opportunities and challenges with autonomous micro aerial vehicles. International Journal of Robotics Research 31(11) (September 2012) 1279–1291
Dellaert, F., Kaess, M.: Square root SAM: Simultaneous localization and mapping via square root information smoothing. International Journal of Robotics Research 25(12) (Dec. 2006) 1181–1203
Lupton, T., Sukkarieh, S.: Visual-inertial-aided navigation for high-dynamic motion in built environments without initial conditions. IEEE Transactions on Robotics 28(1) (February 2012) 61–76
Forster, C., Carlone, L., Dellaert, F., Scaramuzza, D.: Imu preintegration on manifold for efficient visual-inertial maximum-a-posteriori estimation. In: Robotics: Science and Systems XI. Number EPFL-CONF-214687 (2015)
Ling, Y., Liu, T., Shen, S.: Aggressive quadrotor flight using dense visual-inertial fusion. In: 2016 IEEE International Conference on Robotics and Automation (ICRA). (May 2016) 1499–1506
Mourikis, A.I., Roumeliotis, S.I.: A multi-state constraint Kalman filter for vision-aided inertial navigation. In: Proceedings of the IEEE International Conference on Robotics and Automation, Rome, Italy (April 10–14, 2007) 3565–3572
Roumeliotis, S.I., Burdick, J.W.: Stochastic cloning: A generalized framework for processing relative state measurements. In: Proceedings of the IEEE International Conference on Robotics and Automation, Washington, DC (May 11–15 2002) 1788–1795
Huang, G., Eckenhoff, K., Leonard, J.: Optimal-state-constraint EKF for visual-inertial navigation. In: Proc. of the International Symposium on Robotics Research, Sestri Levante, Italy (September 12–15, 2015)
Kümmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard,W.: g2o: A general framework for graph optimization. In: Proc. of the IEEE International Conference on Robotics and Automation, Shanghai, China (May 9–13, 2011) 3607–3613
Indelman, V.,Williams, S., Kaess,M., Dellaert, F.: Factor graph based incremental smoothing in inertial navigation systems. In: Proc. of the International Conference on Information Fusion, Singapore (July 2012)
Indelman, V.,Melim, A., Dellaert, F.: Incremental light bundle adjustment for robotics navigation. In: 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, IEEE (2013) 1952–1959
Trawny, N., Roumeliotis, S.I.: Indirect Kalman filter for 3D attitude estimation. Technical report, University of Minnesota, Dept. of Comp. Sci. & Eng. (March 2005)
Lupton, T., Sukkarieh, S.: Visual-inertial-aided navigation for high-dynamic motion in built environments without initial conditions. IEEE Transactions on Robotics 28(1) (Feb 2012) 61–76
Eckenhoff, K., Geneva, P., Huang, G.: High-accuracy preintegration for visual inertial navigation. Technical Report RPNG-2016-001, University of Delaware (2016) Available: http://udel.edu/~ghuang/papers/tr_hapi.pdf.
Chirikjian, G.S.: Stochastic Models, Information Theory, and Lie Groups, Volume 2: Analytic Methods and Modern Applications. Volume 2. Springer Science & Business Media (2011)
Huang, G.P., Mourikis, A.I., Roumeliotis, S.I.: An observability constrained sliding window filter for SLAM. In: Proc. of the IEEE/RSJ International Conference on Intelligent Robots and Systems, San Francisco, CA (September 25–30, 2011) 65–72
Huang, G., Kaess, M., Leonard, J.: Consistent sparsification for graph optimization. In: Proc. of the European Conference on Mobile Robots, Barcelona, Spain (September 25–27, 2013) 150–157
Forster, C., Pizzoli, M., Scaramuzza, D.: SVO: Fast semi-direct monocular visual odometry. In: Proc. of the IEEE International Conference on Robotics and Automation, Hong Kong, China (May 2014)
Kummerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: g2o: A general framework for graph optimization. In: Proc. of the IEEE International Conference on Robotics and Automation, Shanghai, China (May 9–13, 2011) 3607–3613
Burri, M., Nikolic, J., Gohl, P., Schneider, T., Rehder, J., Omari, S., Achtelik, M.W., Siegwart, R.: The euroc micro aerial vehicle datasets. The International Journal of Robotics Research (2016)
OpenCV Developers Team: Open source computer vision (OpenCV) library. Available: http://opencv.org
Eckenhoff, K., Geneva, P., Huang, G.: Direct visual-inertial navigation with analytical preintegration. In: 2017 IEEE International Conference on Robotics and Automation. (May 2017)
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Eckenhoff, K., Geneva, P., Huang, G. (2020). High-Accuracy Preintegration for Visual-Inertial Navigation. In: Goldberg, K., Abbeel, P., Bekris, K., Miller, L. (eds) Algorithmic Foundations of Robotics XII. Springer Proceedings in Advanced Robotics, vol 13. Springer, Cham. https://doi.org/10.1007/978-3-030-43089-4_4
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