Hostname: page-component-586b7cd67f-l7hp2 Total loading time: 0 Render date: 2024-11-27T19:55:48.992Z Has data issue: false hasContentIssue false
  • English
  • Français

Dynamic GNSS Mission Planning Using DTM for Precise Navigation of Autonomous Vehicles

Published online by Cambridge University Press:  17 October 2016

Aleksander Nowak
Aleksander Nowak*
Affiliation:
(Gdansk University of Technology, Faculty of Civil and Environmental Engineering)
*
(E-mail: [email protected])
Get access Rights & Permissions [Opens in a new window]

Abstract

Nowadays, the most widely used method for estimating location of autonomous vehicles in real time is the use of Global Navigation Satellite Systems (GNSS). However, positioning in urban environments using GNSS is hampered by poor satellite geometry due to signal obstruction created by both man-made and natural features of the urban environment. The presence of obstacles is the reason for the decreased number of observed satellites as well as uncertainty of GNSS positioning. It is possible that in some sections of the vehicle route there might not be enough satellites necessary to fix position. It is common to use software for static GNSS measurement campaign planning, but it is often only able to predict satellite visibility at one point. This article presents a proposal for dynamic GNSS mission planning using a Digital Terrain Model (DTM) and dead reckoning. The methodology and sample results of numerical experiments are also described. They clearly show that proper dynamic GNSS mission planning is necessary in order to complete a task by an autonomous vehicle in an obstructed environment.

Keywords

GNSS availabilityGNSS mission planningAutonomous navigation
Type
Research Article
Information
The Journal of Navigation , Volume 70 , Issue 3 , May 2017 , pp. 483 - 504
Copyright
Copyright © The Royal Institute of Navigation 2016 

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

REFERENCES

Angrisano, A., Gaglione, S., Gioia, C. (2013). Performance assessment of GPS/GLONASS single point positioning in an urban environment. Acta Geodaetica et Geophysica, 48(2), 149–161 Google Scholar
Asavasuthirakul, D., Karimi, H.A. (2013). Integrated GNSS QoS prediction for navigation services. Proceedings of the Sixth ACM SIGSPATIAL International Workshop on Computational Transportation Science, Orlando, FL, 73–78.Google Scholar
Bradbury, J., Ziebart, M., Cross, P.A. (2007). Code Multipath Modelling in the Urban Environment Using Large Virtual Reality City Models: Determining the Local Environment. The Journal of Navigation, 60, 95–105.Google Scholar
Dah, J.J, Chi-Fan, Y., Chih-Hsun, C., Ting-Yu, L. (2013). Performance enhancement for ultra-tight GPS/INS integration using a fuzzy adaptive strong tracking unscented Kalman filter. Nonlinerar Dynamics, 73(1), 377–395 Google Scholar
Dong, G., Yang, Z., Song, L., Ye, K., Li, G., (2015). The Global Shortest Path Visualization Approach with Obstructions, Journal of Robotics and Mechatronics, 27(5), 579–585 Google Scholar
Gibbons, G., Divis, D.A., Gutierrez, P. (2013). The GNSS quartet: Harmonizing GPS, GLONASS, BeiDou and Galileo. Inside GNSS, 8(1), 38–44.Google Scholar
Godha, S., Cannon, M.E. (2007), GPS/MEMS INS integrated system for navigation in urban areas. GPS Solutions, 11(3), 193–203 Google Scholar
Grejner-Brzezinska, D.A., Yi, Y., Toth, C.K. (2001). Bridging GPS Gaps in Urban Canyons: The Benefits of ZUPTs. NAVIGATION. Journal of the Institute of Navigation, 48(4), 216,226.Google Scholar
Janowski, A., Nowak, A., Przyborski, M., Szulwic, J. (2014). Mobile indicators in GIS and GPS positioning accuracy in cities. Rough Sets and Intelligent Systems Paradigms, Lecture Notes in Computer Science, 8537, 309–318.Google Scholar
Karimi, H.A., Asavasuthirakul, D. (2014). A Novel Optimal Routing for Navigation Systems/Services Based on Global Navigation Satellite System Quality of Service. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 18, 286–298.Google Scholar
Kitamura, M., Yasuoka, Y., Suzuki, T., Amano, Y., Hashizume, T. (2013). Path Planning for Autonomous Vehicles Using QZSS and Satellite Visibility Map. Journal of Robotics and Mechatronics, 25(2), 400–407.Google Scholar
Kitamura, M., Suzuki, T., Amano, Y., Hashizume, T. (2011). Improvement of GPS and GLONASS Positioning Accuracy by Multipath Mitigation Using Omni Directional Infrared Camera. Journal of Robotics and Mechatronics, 23(6), 1125–1131.Google Scholar
Li, J., Taylor, G., Kidner, D., Ware, M. (2006). Prediction of GPS multipath effect using LiDAR digital surface models and building footprints. Lecture Notes in Computer Science, 4295, 42–53.Google Scholar
Nowak, A. (2015). The Proposal to “Snapshot” RAIM method for GNSS vessel receivers working in poor space segment geometry, Polish Maritime Research, 4(88), 3–8 Google Scholar
Nowak, A., Specht, C. (2008). Computer Simulator of GNSS Measurements. Annual of Navigation , The Journal of Polish Navigational Forum, 12, 65–74.Google Scholar
Parkinson, B.W., Spilker, J.J. (1996), Global Positioning System: Theory and Applications, Volume II, AIAA, Progress in Astronautics and Aeronautics.Google Scholar
Radisic, T., Novak, D., Bucak, T. (2010). The Effect of Terrain Mask on RAIM Availability. The Journal of Navigation, 63, 105–117.CrossRefGoogle Scholar
Roongpiboonsopit, D., Karimi, H.A. (2012). Integrated global navigation satellite system (iGNSS) QoS prediction. Photogrammetric Engineering & Remote Sensing, 78(2), 139–149.Google Scholar
Soloviev, A., Van Graas, F. (2009). Use of Deeply Integrated GPS/INS Architecture and Laser Scanners for the Identification of Multipath Reflections in Urban Environments. IEEE Journal of Selected Topics in Signal Processing, 3(5), 786–797 CrossRefGoogle Scholar
Suh, Y., Shibasaki, R. (2007). Evaluation of satellite-based navigation services in complex urban environments using a threedimensional GIS. IEICE Transactions on Communications, 90(7), 1816–1825.Google Scholar
Taylor, G., Li, J., Kidner, D., Brunsdon, C., Ware, M. (2007). Modelling and prediction of GPS availability with digital photogrammetry and LiDAR. International Journal of Geographical Information Science, 21(1), 1–20.CrossRefGoogle Scholar