Skip to main content

Main menu

  • Home
  • Current Issue
  • Archive
  • About Us
    • About NAVIGATION
    • Editorial Board
    • Peer Review Statement
    • Open Access
  • More
    • Email Alerts
    • Info for Authors
    • Info for Subscribers
  • Other Publications
    • ion

User menu

  • My alerts

Search

  • Advanced search
NAVIGATION: Journal of the Institute of Navigation
  • Other Publications
    • ion
  • My alerts
NAVIGATION: Journal of the Institute of Navigation

Advanced Search

  • Home
  • Current Issue
  • Archive
  • About Us
    • About NAVIGATION
    • Editorial Board
    • Peer Review Statement
    • Open Access
  • More
    • Email Alerts
    • Info for Authors
    • Info for Subscribers
  • Follow ion on Twitter
  • Visit ion on Facebook
  • Follow ion on Instagram
  • Visit ion on YouTube
Research ArticleOriginal Article
Open Access

Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter

Weisong Wen, Tim Pfeifer, Xiwei Bai and Li-Ta Hsu
NAVIGATION: Journal of the Institute of Navigation June 2021, 68 (2) 315-331; DOI: https://doi.org/10.1002/navi.421
Weisong Wen
1Hong Kong Polytechnic University, Hung Hom, Hong Kong
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Tim Pfeifer
2Chemnitz University of Technology, Chemnitz, Germany
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: [email protected]
Xiwei Bai
1Hong Kong Polytechnic University, Hung Hom, Hong Kong
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Li-Ta Hsu
1Hong Kong Polytechnic University, Hung Hom, Hong Kong
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • For correspondence: [email protected]
  • Article
  • Figures & Data
  • Supplemental
  • References
  • Info & Metrics
  • PDF
Loading

REFERENCES

  1. ↵
    1. Angrisano, A.
    (2010). GNSS/INS integration methods. (Dottorato di ricerca (PhD) in Scienze Geodetiche e Topografiche Thesis). Universita’degli Studi di Napoli PARTHENOPE, Naples.
  2. ↵
    1. Barfoot, T. D.
    (2017). State Estimation for Robotics: Cambridge University Press.
  3. ↵
    1. Bell, B. M &
    2. Cathey, F.W.
    (1993). The iterated Kalman filter update as a Gauss-Newton method. IEEE Transactions on Automatic Control, 38(2), 294–297. https://doi.org/10.1109/9.250476
    CrossRefWeb of Science
  4. ↵
    1. Bhamidipati, S. &
    2. Gao, Grace Xingxin.
    (2018). Multiple GPS fault detection and isolation using a graph-SLAM framework. Proc. of the 31st International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2018), Miami, FL. https://doi.org/10.33012/2018.16030.
  5. ↵
    1. Chen, D. &
    2. Gao, G. X.
    (2019). Probabilistic graphical fusion of LiDAR, GPS, and 3D building maps for urban UAV navigation. NAVIGATION, 66, 151–168. https://doi.org/10.1002/navi.298
  6. ↵
    1. Crassidis, J. L.,
    2. Markley, F. L., &
    3. Cheng, Y.
    (2007). Survey of nonlinear attitude estimation methods. Journal of Guidance, Control, and Dynamics, 30(1), 12–28. https://doi.org/10.2514/1.22452
    CrossRef
  7. ↵
    1. Dellaert, F. &
    2. Kaess, M.
    (2017). Factor graphs for robot perception. Foundations and Trends® in Robotics, 6(1-2), 1–139. https://www.nowpublishers.com/article/Details/ROB-043
  8. ↵
    1. Deng, Z. J.,
    2. Liu, Y. W.,
    3. Liu, J. X.,
    4. Zhou, X, &
    5. Ci, S.
    (2017). QoE-oriented rate allocation for multipath high-definition video streaming over heterogeneous wireless access networks. Ieee Systems Journal, 11(4), 2524–2535. https://doi.org/10.1109/JSYST.2015.2430893
  9. ↵
    1. Falco, G.,
    2. Pini, M, &
    3. Marucco, G.
    (2017). Loose and tight GNSS/INS integrations: Comparison of performance assessed in real urban scenarios. Sensors, 17(2), 255. https://doi.org/10.3390/s17020255
  10. ↵
    1. Gao, G. &
    2. Lachapelle, G.
    (2008). A novel architecture for ultra-tight HSGPS-INS integration. Positioning, 1(13). https://www.scirp.org/html/381.html
  11. ↵
    1. Gao, H. &
    2. Groves, P. D.
    (2020). Context detection for advanced self-aware navigation using smartphone sensors. arXiv preprint arXiv:2005.07539.
  12. ↵
    1. Groves, P. D.
    (2013). Multipath vs. NLOS signals. Inside GNSS 8(6), 40–42. https://insidegnss.com/multipath-vs-nlos-signals/
    1. Groves, P. D.
    (2013). Principles of GNSS, inertial, and multisensor integrated navigation systems (2nd ed.). Artech.
  13. ↵
    1. Groves, P. D.
    (2015). Principles of GNSS, inertial, and multisensor integrated navigation systems (2nd ed.) [Book review]. IEEE Aerospace and Electronic Systems Magazine, 30(2), 26–27. https://doi.org/10.1109/MAES.2014.14110
  14. ↵
    1. Herrera, A. M,
    2. Suhandri, H.F.,
    3. Realini, E.,
    4. Reguzzoni, M., &
    5. de Lacy, M.C.
    (2016). goGPS: open-source MATLAB software. Gps Solutions, 20(3), 595–603. https://geodesy.noaa.gov/gps-toolbox/goGPS.htm
  15. ↵
    1. Hsu, L. T.,
    2. Jan, S. S.,
    3. Groves, P. D, &
    4. Kubo, N.
    (2015). Multipath mitigation and NLOS detection using vector tracking in urban environments. GPS Solutions, 19(2), 249–262. https://doi.org/10.1007/s10291-014-0384-6
  16. ↵
    1. Huang, J.
    (2016). A study on the use of graph signal processing techniques for satellite-based navigation systems. Proc. of the 2016 International Technical Meeting of the Institute of Navigation, Monterey, CA. https://doi.org/10.33012/2016.13444
  17. ↵
    1. Indelman, V.,
    2. Williams, S.,
    3. Kaess, M., &
    4. Dellaert, F.
    (2012). Factor graph based incremental smoothing in inertial navigation systems. 2012 15th International Conference on Information Fusion, Singapore, 2154–2161. https://ieeexplore.ieee.org/document/6290565
    1. Indelman, V.,
    2. Williams, S.,
    3. Kaess, M., &
    4. Dellaert, F.
    (2013). Information fusion in navigation systems via factor graph based incremental smoothing. Robotics and Autonomous Systems, 61(8), 721–738. https://doi.org/10.1016/j.robot.2013.05.001
    CrossRef
  18. ↵
    1. Julier, S. J. &
    2. Uhlmann, J. K.
    (1997). New extension of the Kalman filter to nonlinear systems. Proc. SPIE 3068, Signal Processing, Sensor Fusion, and Target Recognition VI, (28 July 1997), Orlando, FL. https://doi.org/10.1117/12.280797
  19. ↵
    1. Kaess, M.,
    2. Ranganathan, A., &
    3. Dellaert, F.
    (2008). iSAM: Incremental smoothing and mapping. IEEE Transactions on Robotics, 24(6), 1365–1378. https://doi.org/10.1109/TRO.2008.2006706
  20. ↵
    1. Li, M. &
    2. Mourikis, A. I.
    (2013). Optimization-based estimator design for vision-aided inertial navigation. Robotics: Science and Systems. http://roboticsproceedings.org/rss08/p31.pdf
  21. ↵
    1. Li, W.,
    2. Cui, X., &
    3. Lu, M.
    (2018). A robust graph optimization realization of tightly coupled GNSS/INS integrated navigation system for urban vehicles. Tsinghua Science and Technology, 23(6), 724–732. https://doi.org/10.26599/TST.2018.9010078
  22. ↵
    1. Litman, T.
    (2015). Autonomous vehicle implementation predictions: Implications for transport planning. TRB 94th Annual Meeting Compendium of Papers, Washington, DC. https://trid.trb.org/view.aspx?id=1338043
  23. ↵
    1. Liu, H.,
    2. Nassar, S., &
    3. El-Sheimy, N.
    (2010). Two-filter smoothing for accurate INS/GPS land-vehicle navigation in urban centers. Ieee Transactions on Vehicular Technology, 59(9), 4256–4267. https://doi.org/10.1109/TVT.2010.2070850
  24. ↵
    1. Maier, D., &
    2. Kleiner, A.
    (2010). Improved GPS sensor model for mobile robots in urban terrain. 2010 IEEE International Conference on Robotics and Automation, Anchorage, AK, 4385-4390. https://doi.org/10.1109/ROBOT.2010.5509895
  25. ↵
    1. Petovello, M. G.
    (2003). Real-time integration of a tactical-grade IMU and GPS for high-accuracy positioning and navigation (Unpublished doctoral thesis). University of Calgary, Calgary, AB. https://doi.org/10.11575/PRISM/23031
  26. ↵
    1. Pfeifer, T., &
    2. Protzel, P.
    (2018). Robust sensor fusion with self-tuning mixture models. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain, 3678-3685. https://doi.org/10.1109/IROS.2018.8594459
  27. ↵
    1. Pfeifer, T., &
    2. Protzel, P.
    (2019a). Expectation-maximization for adaptive mixture models in graph optimization. 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada. https://doi.org/10.1109/ICRA.2019.8793601
  28. ↵
    1. Pfeifer, T., &
    2. Protzel, P.
    (2019b). Incrementally learned mixture models for GNSS localization. 2019 IEEE Intelligent Vehicles Symposium (IV), Paris, France, 1131-1138. https://doi.org/10.1109/IVS.2019.8813847
  29. ↵
    1. Qin, T.,
    2. Li, P., &
    3. Shen, S.
    (2018). Vins-mono: A robust and versatile monocular visual-inertial state estimator. IEEE Transactions on Robotics, 34(4), 1004–1020. https://doi.org/10.1109/TRO.2018.2853729
  30. ↵
    1. Quigley, M.,
    2. Conley, K.,
    3. Gerkey, B.,
    4. Faust, J.,
    5. Foote, T.,
    6. Leibs, J.,
    7. Wheeler, R., &
    8. Ng, A. Y.
    (2009). ROS: an open-source Robot Operating System. ICRA Workshop on open source software. http://robotics.stanford.edu/∼ang/papers/icraoss09-ROS.pdf
  31. ↵
    1. Roysdon, P. F., &
    2. Farrell, J. A.
    (2017). GPS-INS outlier detection & elimination using a sliding window filter. 2017 American Control Conference (ACC), Seattle, WA, 1244-1249. https://doi.org/10.23919/ACC.2017.7963123
  32. ↵
    1. Saripalli, S.,
    2. Montgomery, J. F., &
    3. Sukhatme, G. S.
    (2003). Visually guided landing of an unmanned aerial vehicle. IEEE transactions on robotics and automation, 19(3), 371–380. https://doi.org/10.1109/TRA.2003.810239
    CrossRef
  33. ↵
    1. Solimeno, A.
    (2007). Low-cost INS/GPS data fusion with extended Kalman filter for airborne applications. (Masters of Science thesis, Universidade Technica de Lisboa, Lisbon, Portugal). https://www.semanticscholar.org/paper/Low-Cost-INS%2FGPS-Data-Fusion-with-Extended-Kalman-Solimeno/1dd8f11cb300b9dd134e0de1bb46eefb4b12f075
  34. ↵
    1. Thrun, S.
    (2000). Probabilistic algorithms in robotics. AI Magazine, 21(4), 93. https://doi.org/10.1609/aimag.v21i4.1534
  35. ↵
    1. Valiente, D.,
    2. Gil, A.,
    3. Fernández, L., &
    4. Reinoso, Ó.
    (2014). A comparison of EKF and SGD applied to a view-based SLAM approach with omnidirectional images. Robotics and Autonomous Systems, 62(2), 108–119. https://doi.org/10.1016/j.robot.2013.11.009
  36. ↵
    1. Wan, E. A. &
    2. Van Der Merwe, R.
    (2000). The unscented Kalman filter for nonlinear estimation. Proc. of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373), Lake Louise, AB, Canada, 153-158. https://doi.org/10.1109/ASSPCC.2000.882463
  37. ↵
    1. Wan, G.,
    2. Yang, X.,
    3. Cai, R.,
    4. Li, H.,
    5. Zhou, Y.,
    6. Wang, H., &
    7. Song, S.
    (2018). Robust and precise vehicle localization based on multi-sensor fusion in diverse city scenes. 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, 4670-4677. https://doi.org/10.1109/ICRA.2018.8461224
  38. ↵
    1. Watson, R. M., &
    2. Gross, J. N.
    (2018). Evaluation of kinematic precise point positioning convergence with an incremental graph optimizer. 2018 IEEE/ION Position, Location and Navigation Symposium (PLANS), Monterey, CA. https://arxiv.org/pdf/1804.04197.pdf
  39. ↵
    1. Welch, G., &
    2. Bishop, G.
    (1995). An introduction to the Kalman filter. https://perso.crans.org/club-krobot/doc/kalman.pdf
  40. ↵
    1. Wen, W.,
    2. Bai, X.,
    3. Kan, Y.-C., &
    4. Hsu, L.-T.
    (2019). Tightly coupled GNSS/INS integration via factor graph and aided by fish-eye camera. IEEE Transactions on Vehicular Technology. 68(11):10651–10652. https://doi.org/10.1109/TVT.2019.2944680
  41. ↵
    1. Wen, W.,
    2. Hsu, L.-T., &
    3. Zhang, G.
    (2018). Performance analysis of NDT-based graph SLAM for autonomous vehicle in diverse typical driving scenarios of Hong Kong. Sensors, 18(11), 3928. https://doi.org/10.3390/s18113928
    1. Wen, W.,
    2. Kan, Y. C., &
    3. Hsu, L.-T.
    (2019). Performance comparison of GNSS/INS integrations based on EKF and factor graph optimization. Proc. of the 32nd International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2019), Miami, FL. https://doi.org/10.33012/2019.17129
  42. ↵
    1. Wen, W.,
    2. Zhou, Y.,
    3. Zhang, G.,
    4. Fahandezh-Saadi, S.,
    5. Bai, X.,
    6. Zhan, W.,
    7. Tomizuka, M., &
    8. Hsu, L.-T.
    (2020). Urbanloco: a full sensor suite dataset for mapping and localization in urban scenes. 2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, 2310-2316. https://doi.org/10.1109/ICRA40945.2020.9196526
  43. ↵
    1. Xu, B.,
    2. Jia, Q.,
    3. Luo, Y., &
    4. Hsu, L.-T.
    (2019). Intelligent GPS L1 LOS/multipath/NLOS classifiers based on correlator-, RINEX-and NMEA-level measurements. Remote Sensing, 11(16), 1851. https://doi.org/10.3390/rs11161851
  44. ↵
    1. Zhao, S.,
    2. Chen, Y., &
    3. Farrell, J. A.
    (2016). High-precision vehicle navigation in urban environments using an MEM’s IMU and single-frequency GPS receiver. IEEE Transactions on Intelligent Transportation Systems, 17(10), 2854–2867. https://doi.org/10.1109/TITS.2016.2529000
  45. ↵
    1. Zhao, S.,
    2. Chen, Y.,
    3. Zhang, H., &
    4. Farrell, J. A.
    (2014). Differential GPS aided inertial navigation: A contemplative realtime approach. IFAC Proceedings Volumes, 47(3), 8959–8964. https://folk.ntnu.no/skoge/prost/proceedings/ifac2014/media/files/1658.pdf
  46. ↵
    1. Zheng Gong, P. L.,
    2. Qiang Liu,
    3. Ruihang Miao, and
    4. Rendong Ying
    . (2018). Tightly coupled GNSS with stereo camera navigation using graph optimization. Proc. of the 31st International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2018), Miami, FL, 3070–3077. https://doi.org/10.33012/2018.15959
  47. ↵
    1. Zhuang, Y., &
    2. El-Sheimy, N.
    (2015). Tightly-coupled integration of WiFi and MEMS sensors on handheld devices for indoor pedestrian navigation. IEEE Sensors Journal, 16(1), 224–234. https://doi.org/10.1109/JSEN.2015.2477444
  48. ↵
    1. Zhuang, Y.,
    2. Li, Y.,
    3. Qi, L.,
    4. Lan, H.,
    5. Yang, J., &
    6. El-Sheimy, N.
    (2016). A two-filter integration of MEMS sensors and WiFi fingerprinting for indoor positioning. IEEE Sensors Journal, 16(13), 5125–5126. https://doi.org/10.1109/JSEN.2016.2567224
PreviousNext
Back to top

In this issue

NAVIGATION: Journal of the Institute of Navigation: 68 (2)
NAVIGATION: Journal of the Institute of Navigation
Vol. 68, Issue 2
Summer 2021
  • Table of Contents
  • Index by author
Print
Download PDF
Article Alerts
Sign In to Email Alerts with your Email Address
Email Article

Thank you for your interest in spreading the word on NAVIGATION: Journal of the Institute of Navigation.

NOTE: We only request your email address so that the person you are recommending the page to knows that you wanted them to see it, and that it is not junk mail. We do not capture any email address.

Enter multiple addresses on separate lines or separate them with commas.
Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter
(Your Name) has sent you a message from NAVIGATION: Journal of the Institute of Navigation
(Your Name) thought you would like to see the NAVIGATION: Journal of the Institute of Navigation web site.
Citation Tools
Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter
Weisong Wen, Tim Pfeifer, Xiwei Bai, Li-Ta Hsu
NAVIGATION: Journal of the Institute of Navigation Jun 2021, 68 (2) 315-331; DOI: 10.1002/navi.421

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
Share
Factor graph optimization for GNSS/INS integration: A comparison with the extended Kalman filter
Weisong Wen, Tim Pfeifer, Xiwei Bai, Li-Ta Hsu
NAVIGATION: Journal of the Institute of Navigation Jun 2021, 68 (2) 315-331; DOI: 10.1002/navi.421
Reddit logo Twitter logo Facebook logo Mendeley logo
  • Tweet Widget
  • Facebook Like
  • Google Plus One
Bookmark this article

Jump to section

  • Article
    • Abstract
    • 1 INTRODUCTION
    • 2 RELATED WORK
    • 3 METHODOLOGY
    • 4 EXPERIMENT EVALUATION
    • 5 CONCLUSIONS AND FUTURE WORK
    • HOW TO CITE THIS ARTICLE
    • APPENDIX A: TRANSFORMATION MATRIX
    • Footnotes
    • REFERENCES
  • Figures & Data
  • Supplemental
  • References
  • Info & Metrics
  • PDF

Related Articles

  • No related articles found.
  • Google Scholar

Cited By...

  • No citing articles found.
  • Google Scholar

More in this TOC Section

  • Ambiguity-Fixing in Frequency-Varying Carrier Phase Measurements: Global Navigation Satellite System and Terrestrial Examples
  • PPP/PPP-RTK Message Authentication
  • Resilient Smartphone Positioning Using Native Sensors and PPP Augmentation
Show more Original Article

Similar Articles

Keywords

  • extended Kalman filter
  • factor graph optimization
  • GNSS
  • INS
  • integration
  • navigation
  • positioning
  • urban canyons
  • window size

Unless otherwise noted, NAVIGATION content is licensed under a Creative Commons CC BY 4.0 License.

© 2023 The Institute of Navigation, Inc.

Powered by HighWire