Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-29T08:00:59.943Z Has data issue: false hasContentIssue false
  • English
  • Français

Compressed pseudo-SLAM: pseudorange-integrated compressed simultaneous localisation and mapping for unmanned aerial vehicle navigation

Published online by Cambridge University Press:  26 March 2021

Jonghyuk Kim ,
Jose Guivant ,
Martin L. Sollie ,
Torleiv H. Bryne Open the ORCID record for Torleiv H. Bryne [Opens in a new window]  and
Tor Arne Johansen
Jonghyuk Kim*
Affiliation:
Centre for Autonomous Systems, University of Technology Sydney, Sydney, NSW, Australia.
Jose Guivant
Affiliation:
School of Mechanical and Manufacturing Engineering, University of New South Wales, Sydney, NSW, Australia.
Martin L. Sollie
Affiliation:
Centre for Autonomous Marine Operations and Systems, Norwegian University of Science and Technology, Trondheim, Norway
Torleiv H. Bryne
Affiliation:
Centre for Autonomous Marine Operations and Systems, Norwegian University of Science and Technology, Trondheim, Norway
Tor Arne Johansen
Affiliation:
Centre for Autonomous Marine Operations and Systems, Norwegian University of Science and Technology, Trondheim, Norway
*
*Corresponding author. E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

This paper addresses the fusion of the pseudorange/pseudorange rate observations from the global navigation satellite system and the inertial–visual simultaneous localisation and mapping (SLAM) to achieve reliable navigation of unmanned aerial vehicles. This work extends the previous work on a simulation-based study [Kim et al. (2017). Compressed fusion of GNSS and inertial navigation with simultaneous localisation and mapping. IEEE Aerospace and Electronic Systems Magazine, 32(8), 22–36] to a real-flight dataset collected from a fixed-wing unmanned aerial vehicle platform. The dataset consists of measurements from visual landmarks, an inertial measurement unit, and pseudorange and pseudorange rates. We propose a novel all-source navigation filter, termed a compressed pseudo-SLAM, which can seamlessly integrate all available information in a computationally efficient way. In this framework, a local map is dynamically defined around the vehicle, updating the vehicle and local landmark states within the region. A global map includes the rest of the landmarks and is updated at a much lower rate by accumulating (or compressing) the local-to-global correlation information within the filter. It will show that the horizontal navigation error is effectively constrained with one satellite vehicle and one landmark observation. The computational cost will be analysed, demonstrating the efficiency of the method.

Keywords

aerial SLAMcompressed filteringpseudorange integrationUAV navigation
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 5 , September 2021 , pp. 1091 - 1103
Copyright
Copyright © The Royal Institute of Navigation 2021

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

Guivant, J. E. (2017). The generalized compressed Kalman filter. Robotica, 35(8), 1639–1669.CrossRefGoogle Scholar
Kim, J. and Sukkarieh, S. (2003). Airborne Simultaneous Localisation and Map Building. Proceedings. IEEE International Conference on Robotics and Automation, 2003, vol. 1, 406–411. IEEE.Google Scholar
Kim, J. and Sukkarieh, S. (2007). Real-time implementation of airborne inertial-SLAM. Robotics and Autonomous Systems, 55(1), 62–71.CrossRefGoogle Scholar
Kim, J., Sukkarieh, S and Wishart, S. (2003). Real-time Navigation, Guidance and Control of a UAV using Low-cost Sensors. International Conference of Field and Service Robotics, Yamanashi, Japan, 95–100.Google Scholar
Kim, J., Cheng, J., Guivant, J. and Nieto, J. (2017). Compressed fusion of GNSS and inertial navigation with simultaneous localization and mapping. IEEE Aerospace and Electronic Systems Magazine, 32(8), 22–36.CrossRefGoogle Scholar
Li, M. and Mourikis, A. I. (2013). High-precision, consistent EKF-based visual-inertial odometry. The International Journal of Robotics Research, 32(6), 690–711.CrossRefGoogle Scholar
Nützi, G., Weiss, S., Scaramuzza, D. and Siegwart, R. (2011). Fusion of IMU and vision for absolute scale estimation in monocular SLAM. Journal of intelligent & robotic systems, 61(1–4), 287–299.CrossRefGoogle Scholar
Parkinson, B. (1997). Origins, evolution, and future of satellite navigation. Journal of Guidance, Control, and Dynamics, 20(1), 11–25.CrossRefGoogle Scholar
Sjanic, Z., Skoglund, M. A. and Gustafsson, F. (2017). EM-SLAM with inertial/visual applications. IEEE Transactions on Aerospace and Electronic Systems, 53(1), 273–285.CrossRefGoogle Scholar
Skulstad, R., Syversen, C., Merz, M., Sokolova, N., Fossen, T. and Johansen, T. (2015). Autonomous net recovery of fixed-wing UAV with single-frequency carrier-phase differential GNSS. IEEE Aerospace and Electronic Systems Magazine, 30(5), 18–27.CrossRefGoogle Scholar
Vidal, A. R., Rebecq, H., Horstschaefer, T. and Scaramuzza, D. (2018). Ultimate SLAM? Combining events, images, and IMU for robust visual SLAM in HDR and high-speed scenarios. IEEE Robotics and Automation Letters, 3(2), 994–1001.CrossRefGoogle Scholar
Williams, P. and Crump, M. (2012). All-source Navigation for Enhancing UAV Operations in GPS-denied Environments. Proceedings of the 28th International Congress of the Aeronautical Sciences. International Council of The Aeronautical Sciences.Google Scholar