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Tsinghua Science and Technology 2018, 23(6): 724-732 https://doi.org/10.26599/TST.2018.9010078
Open Access | Issue | Published: 15 October 2018

A Robust Graph Optimization Realization of Tightly Coupled GNSS/INS Integrated Navigation System for Urban Vehicles

Show Author's Information Wei Li,  Xiaowei Cui( ),  Mingquan Lu
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.
Keywords:
optimization, sensor fusion, Global Navigation Satellite System (GNSS), factor graph, Inertial Navigation System (INS), tightly coupled integration
Cite this article:
Li W, Cui X, Lu M. A Robust Graph Optimization Realization of Tightly Coupled GNSS/INS Integrated Navigation System for Urban Vehicles. Tsinghua Science and Technology, 2018, 23(6): 724-732. https://doi.org/10.26599/TST.2018.9010078
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Abstract Full text About this article

This paper describes a robust integrated positioning method to provide ground vehicles in urbanenvironments with accurate and reliable localization results. The localization problem is formulated as a maximum a posteriori probability estimation and solved using graph optimization instead of Bayesian filter. Graph optimization exploits the inherent sparsity of the observation process to satisfy the real-time requirement and only updates the incremental portion of the variables with each new incoming measurement. Unlike the Extended Kalman Filter (EKF) in a typical tightly coupled Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) integrated system, optimization iterates the solution for the entire trajectory. Thus, previous INS measurements may provide redundant motion constraints for satellite fault detection. With the help of data redundancy, we add a new variable that presents reliability of GNSS measurement to the original state vector for adjusting the weight of corresponding pseudorange residual and exclude faulty measurements. The proposed method is demonstrated on datasets with artificial noise, simulating a moving vehicle equipped with GNSS receiver and inertial measurement unit. Compared with the solutions obtained by the EKF with innovation filtering, the new reliability factor can indicate the satellite faults effectively and provide successful positioning despite contaminated observations.


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About this article

A Robust Graph Optimization Realization of Tightly Coupled GNSS/INS Integrated Navigation System for Urban Vehicles

Show Author's information Wei Li, Xiaowei Cui( ), Mingquan Lu
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China.

Abstract

This paper describes a robust integrated positioning method to provide ground vehicles in urbanenvironments with accurate and reliable localization results. The localization problem is formulated as a maximum a posteriori probability estimation and solved using graph optimization instead of Bayesian filter. Graph optimization exploits the inherent sparsity of the observation process to satisfy the real-time requirement and only updates the incremental portion of the variables with each new incoming measurement. Unlike the Extended Kalman Filter (EKF) in a typical tightly coupled Global Navigation Satellite System/Inertial Navigation System (GNSS/INS) integrated system, optimization iterates the solution for the entire trajectory. Thus, previous INS measurements may provide redundant motion constraints for satellite fault detection. With the help of data redundancy, we add a new variable that presents reliability of GNSS measurement to the original state vector for adjusting the weight of corresponding pseudorange residual and exclude faulty measurements. The proposed method is demonstrated on datasets with artificial noise, simulating a moving vehicle equipped with GNSS receiver and inertial measurement unit. Compared with the solutions obtained by the EKF with innovation filtering, the new reliability factor can indicate the satellite faults effectively and provide successful positioning despite contaminated observations.

Keywords: optimization, sensor fusion, Global Navigation Satellite System (GNSS), factor graph, Inertial Navigation System (INS), tightly coupled integration

References(22)

[1]
Lee Y. C., Receiver autonomous integrity monitoring availability for GPS augmented with barometric altimeter aiding and clock coasting, Global Positioning System: Theory and Applications, vol. 2, pp. 221–242, 1996.
DOI Google Scholar
[2]
Jan S. S., Gebre-Egziabher D., Walter T., and Enge P., Improving GPS-based landing system performance using an empirical barometric altimeter confidence bound, IEEE Transactions on Aerospace and Electronic Systems, vol. 44, no. 1, pp. 127–146, 2008.
DOI Google Scholar
[3]
Bhatti U. I., Ochieng W. Y., and Feng S., Integrity of an integrated GPS/INS system in the presence of slowly growing errors. Part I: A critical review, GPS Solutions, vol. 11, no. 3, pp. 173–181, 2007.
DOI Google Scholar
[4]
Dusha D. and Mejias L., Error analysis and attitude observability of a monocular GPS/visual odometry integrated navigation filter, The International Journal of Robotics Research, vol. 31, no. 6, pp. 714–737, 2012.
DOI Google Scholar
[5]
Abbott A. S. and Lillo W. E., Global positioning systems and inertial measuring unit ultratight coupling method, U.S. Patent 6 516 021, Feb. 2003.
[6]
Falco G., Pini M., and Marucco G., Loose and tight GNSS/INS integrations: Comparison of performance assessed in real urban scenarios, Sensors, vol. 17, no. 2, 2017.
DOI Google Scholar
[7]
Farrell J., Aided Navigation: GPS with High Rate Sensors, 1st ed. New York, NY, USA: McGraw-Hill, Inc., 2008.
[8]
Crassidis J. L., Sigma-point Kalman filtering for integrated GPS and inertial navigation, IEEE Transactions on Aerospace and Electronic Systems, vol. 42, no. 2, pp. 750–756, 2006.
DOI Google Scholar
[9]
Barfoot T. D., State Estimation for Robotics. Cambridge University Press, 2017.
DOI
[10]
Sibley G., Matthies L., and Sukhatme G., A sliding window filter for incremental SLAM, in Unifying Perspectives in Computational and Robot Vision, Kragic D. and Kyrki V.,eds. Springer, 2008, pp. 103–112.
DOI
[11]
Indelman V., Williams S., Kaess M., and Dellaert F., Factor graph based incremental smoothing in inertial navigation systems, in 2012 15th International Conference on Information Fusion, 2012, pp. 2154–2161.
[12]
Kschischang F. R., Frey B. J., and Loeliger H. A., Factor graphs and the sum-product algorithm, IEEE Transactions on Information Theory, vol. 47, no. 2, pp. 498–519, 2001.
DOI Google Scholar
[13]
Folkesson J. and Christensen H., Graphical SLAM—A self-correcting map, in Proc. IEEE Intl. Conf. Robot. Autom. (ICRA), 2004, vol. 1, pp. 383–390.
DOI
[14]
Kaess M., Johannsson H., Roberts R., Ila V., Leonard J. J., and Dellaert F., iSAM2: Incremental smoothing and mapping using the Bayes tree, The International Journal of Robotics Research, vol. 31, no. 2, pp. 216–235, 2012.
DOI Google Scholar
[15]
Parkinson B. W. and Axelrad P., Autonomous GPS integrity monitoring using the pseudorange residual, Navigation, vol. 35, no. 2, pp. 255–274, 1988.
DOI Google Scholar
[16]
Sturza M. A., Navigation system integrity monitoring using redundant measurements, Navigation, vol. 35, no. 4, pp. 483–501, 1988.
DOI Google Scholar
[17]
Groves P. D., Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems. Norwood, MA, USA: Artech House, 2008.
[18]
Sünderhauf N., Obst M., Wanielik G., and Protzel P., Multipath mitigation in GNSS-based localization using robust optimization, in 2012 IEEE Intelligent Vehicles Symposium, 2012, pp. 784–789.
DOI
[19]
Sünderhauf N., Obst M., Lange S., Wanielik G., and Protzel P., Switchable constraints and incremental smoothing for online mitigation of non-line-of-sight and multipath effects, in 2013 IEEE Intelligent Vehicles Symposium (IV), 2013, pp. 262–268.
DOI
[20]
Sünderhauf N. and Protzel P., Switchable constraints for robust pose graph SLAM, in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012, pp. 1879–1884.
DOI
[21]
Kaplan E. and Hegarty C., Understanding GPS: Principles and Applications. Norwood, MA, USA: Artech house, 2005.
[22]
Kmmerle R., Grisetti G., Strasdat H., Konolige K., and Burgard W., g2o: A general framework for graph optimization, in 2011 IEEE International Conference on Robotics and Automation, 2011, pp. 3607–3613.
DOI
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Received: 19 December 2017
Accepted: 18 January 2018
Published: 15 October 2018
Issue date: December 2018

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