Hostname: page-component-586b7cd67f-dlnhk Total loading time: 0 Render date: 2024-11-23T05:09:18.368Z Has data issue: false hasContentIssue false
  • English
  • Français

A new visual/inertial integrated navigation algorithm based on sliding-window factor graph optimisation

Published online by Cambridge University Press:  01 December 2020

Haiying Liu ,
Jingqi Wang ,
Jianxin Feng  and
Xinyao Wang
Haiying Liu*
Affiliation:
College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing210016, P. R. China
Jingqi Wang
Affiliation:
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing210016, P. R. China
Jianxin Feng
Affiliation:
College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing210016, P. R. China
Xinyao Wang
Affiliation:
College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing210016, P. R. China
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Visual–Inertial Navigation Systems (VINS) plays an important role in many navigation applications. In order to improve the performance of VINS, a new visual/inertial integrated navigation method, named Sliding-Window Factor Graph optimised algorithm with Dynamic prior information (DSWFG), is proposed. To bound computational complexity, the algorithm limits the scale of data operations through sliding windows, and constructs the states to be optimised in the window with factor graph; at the same time, the prior information for sliding windows is set dynamically to maintain interframe constraints and ensure the accuracy of the state estimation after optimisation. First, the dynamic model of vehicle and the observation equation of VINS are introduced. Next, as a contrast, an Invariant Extended Kalman Filter (InEKF) is constructed. Then, the DSWFG algorithm is described in detail. Finally, based on the test data, the comparison experiments of Extended Kalman Filter (EKF), InEKF and DSWFG algorithms in different motion scenes are presented. The results show that the new method can achieve superior accuracy and stability in almost all motion scenes.

Keywords

visual–inertial navigationInEKFfactor graph optimisationsliding-window filter
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 2 , March 2021 , pp. 343 - 359
Check for updates
Copyright
Copyright © The Royal Institute of Navigation 2020

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Barrau, A. (2015). Non-linear state error based extended Kalman filters with applications to navigation (PhD thesis). Mines Paristech.Google Scholar
Dellaert, F. and Kaess, M. (2017). Factor graphs for robot perception. Foundations and Trends in Robotics, 6(1–2), 1–139.CrossRefGoogle Scholar
Dong-Si, T. and Mourikis, A. I. (2011). Motion Tracking with Fixed-lag Smoothing: Algorithm and Consistency Analysis. 2011 IEEE International Conference on Robotics and Automation. Shanghai, 5655–5662.CrossRefGoogle Scholar
Eckenhoff, K., Geneva, P. and Huang, G. (2017). Direct Visual-Inertial Navigation With Analytical Preintegration. 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, Singapore, 1429–1435.CrossRefGoogle Scholar
Forster, C., Carlone, L., Dellaert, F. and Scaramuzza, D. (2017). On-manifold preintegration for real-time visual–inertial odometry. IEEE Transactions on Robotics, 33(1), 1–21.CrossRefGoogle Scholar
Geiger, A., Lenz, P. and Urtasun, R. (2012). Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite. 2012 IEEE Conference on Computer Vision and Paattern Recognition(CVPR). RI, USA: IEEE, 3354–3361.CrossRefGoogle Scholar
Geiger, A., Lenz, P., Stiller, C. and Urtasun, R. (2013). Vision meets Robotics: the KITTI Dataset. The Internaltional Journal of Robotics Research(IJRR, 32(11), 1231–1237.CrossRefGoogle Scholar
Hesch, J. A., Kottas, D. G., Bowman, S. L. and Roumeliotis, S. I. (2014). Consistency analysis and improvement of vision-aided inertial navigation. IEEE Transactions on Robotics, 30(1), 158–176.CrossRefGoogle Scholar
Hsiung, J., Hsiao, M., Westman, E., Valencia, R. and Kaess, M. (2018). Information Sparsification in Visual-Inertial Odometry. 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Madrid, Spain, 1146–1153.CrossRefGoogle Scholar
Huang, G. (2019). Visual-Inertial Navigation: A Concise Review. 2019 International Conference on Robotics and Automation (ICRA). Montreal, Canada, 9572–9582.CrossRefGoogle Scholar
Huang, G. P., Mourikis, A. I. and Roumeliotis, S. I. (2009). A First-Estimates Jacobian EKF for Improving SLAM Consistency. Experimental Robotics, Berlin, 373–382.CrossRefGoogle Scholar
Jiang, J., Niu, X., Guo, R. and Liu, J. (2019). A hybrid sliding window optimizer for tightly-coupled vision-aided inertial navigation system. Sensors (Basel), 19(15), 3418–3439.CrossRefGoogle ScholarPubMed
Johannsson, H., Kaess, M., Fallon, M. and Leonard, J. J. (2013). Temporally Scalable Visual SLAM Using a Reduced Pose Graph. In Proceedings of the IEEE International Conference on Robotics and Automation(ICRA), Karlsruhe, 54–61.CrossRefGoogle Scholar
Kretzschmar, H., Stachniss, C. and Grisett, G. (2011). Efficient Information-Theoretic Graph Pruning for Graph-Based SLAM With Laser Range Finders. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), San Francisco, 865–871.CrossRefGoogle Scholar
Li, M. and Mourikis, A. I. (2013). High-precision, consistent EKF-based visual-inertial odometry[J]. The International Journal of Robotics Research, 32(6), 690–711.CrossRefGoogle Scholar
Mourikis, A. I. and Roumeliotis, S. I. (2007). A Multi-State Constraint Kalman Filter for Vision-Aided Inertial Navigation. Proceedings 2007 IEEE International Conference on Robotics and Automation, Italy, 3565–3572.CrossRefGoogle Scholar
Richard, H. and Zisserman, A. (2000). Multiple View Geometry in Computer Vision[M]. New York, UK: Cambridge University Press, 217–220.Google Scholar
Sibley, G., Sukhatme, G. and Matthies, L. (2006). The iterated sigma point Kalman filter with applications to long range stereo. In Robotics:Science and Systems, 8, 263–270.Google Scholar
Sibley, G., Matthies, L. and Sukhatme, G. S. (2010). Sliding window filter with application to planetary landing. Journal of Field Robotics, 27(5), 1556–4959.CrossRefGoogle Scholar
Wilbers, D., Rumberg, L. and Stachniss, C. (2019). Approximating Marginalization with Sparse Global Priors for Sliding Window SLAM-Graphs. 2019 Third IEEE International Conference on Robotic Computing (IRC), Italy, 25–31.CrossRefGoogle Scholar
Wu, K., Zhang, T., Su, D., Huang, S. and Dissanayake, G. (2017). An invariant-EKF VINS algorithm for improving consistency. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, 1578–1585.CrossRefGoogle Scholar
Zhuang, Y., Wang, Q., Li, Y., Gao, Z., Zhou, B., Qi, L., Yang, J., Chen, R. and El-Sheimy, N. (2019). The integration of photodiode and camera for visible light positioning by using fixed-lag ensemble Kalman smoother. Remote Sensing, 11(11), 1387.CrossRefGoogle Scholar