Hostname: page-component-78c5997874-t5tsf Total loading time: 0 Render date: 2024-11-11T01:48:40.420Z Has data issue: false hasContentIssue false
  • English
  • Français

Precise Single-Frequency Positioning Using Low-Cost Receiver with the Aid of Lane-Level Map Matching for Land Vehicle Navigation

Published online by Cambridge University Press:  23 July 2020

Fei Liu ,
Yue Liu ,
Zhixi Nie  and
Yang Gao
Fei Liu*
Affiliation:
(Department of Civil Engineering, University of Calgary, Calgary, Alberta, Canada)
Yue Liu
Affiliation:
(College of Automation, Harbin Engineering University, Harbin, Heilongjiang, China)
Zhixi Nie
Affiliation:
(College of Oceanography and Space Informatics, China University of Petroleum, Qingdao, China)
Yang Gao
Affiliation:
(Department of Geomatics Engineering, University of Calgary, Calgary, Alberta, Canada)
*
(E-mail: liu19@ucalgary.ca)
Get access Rights & Permissions [Opens in a new window]

Abstract

Precise positioning with low-cost single-frequency global navigation satellite system (GNSS) receivers has great potential in a wide range of applications because of its low price and improved accuracy. However, challenges remain in achieving reliable and accurate solutions using low-cost receivers. For instance, the successful ambiguity fixing rate could be low for real-time kinematic (RTK) while large errors may occur in precise point positioning (PPP) in some scenarios (e.g., trees along the road). To solve the problems, this paper proposes a method with the aid of additional lane-level digital map information to improve the accuracy and reliability of RTK and PPP solutions. In the method, a digital camera will be applied for lane recognition and the positioning solution from a low-cost receiver will be projected to the digital map lane link. With the projected point position as a constraint, the RTK ambiguity fixing rate and PPP performance can be enhanced. A field kinematic test was conducted to verify the improvement of the RTK and PPP solutions with the aid of map matching. The results show that the RTK ambiguity fixing rate can be increased and the PPP positioning error can be reduced by map matching.

Keywords

Single FrequencyLow CostMap MatchingPrecise Point PositioningReal-Time Kinematic
Type
Research Article
Information
The Journal of Navigation , Volume 74 , Issue 1 , January 2021 , pp. 24 - 37
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

REFERENCES

Ashby, N. (2003). Relativity in the global positioning system. Living Reviews in Relativity, 6(1), 1.CrossRefGoogle ScholarPubMed
Balazadegan Sarvrood, Y., Liu, F. and Gao, Y. (2018). Tight integration of kinematic precise point positioning and digital map for land vehicle localisation. Survey Review, 50(362), 416–424.CrossRefGoogle Scholar
CNES. (2019). The PPP-WIZARD project. Available at: http://www.ppp-wizard.net/ssr.html.Google Scholar
de Bakker, P. F. and Tiberius, C. C. J. M. (2017). Real-time multi-GNSS single-frequency precise point positioning. GPS Solutions, 21(4), 1791–1803.CrossRefGoogle Scholar
De Brabandere, B., Neven, D. and Van Gool, L. (2017). Semantic instance segmentation with a discriminative loss function. arXiv preprint arXiv:1708.02551.Google Scholar
Han, H., et al. (2017). Reliable partial ambiguity resolution for single-frequency GPS/BDS and INS integration. GPS Solutions, 21(1), 251–264.CrossRefGoogle Scholar
Li, T., Zhang, H., Niu, X. and Gao, Z. (2017). Tightly-coupled integration of multi-GNSS single-frequency RTK and MEMS-IMU for enhanced positioning performance. Sensors, 17(11), 2462.CrossRefGoogle ScholarPubMed
Liu, F. (2017). Kinematic PPP Ambiguity Resolution with Aid of Map Matching. In Proceedings of the 30th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2017). Portland, OR, pp. 4176–4183.CrossRefGoogle Scholar
Liu, Y., Liu, F., Gao, Y. and Zhao, L. (2018). Implementation and analysis of tightly coupled global navigation satellite system precise point positioning/inertial navigation system (GNSS PPP/INS) with insufficient satellites for land vehicle navigation. Sensors, 18(12), 4305.CrossRefGoogle ScholarPubMed
MaybeShewill-CV. (2019) LaneNet-Lane-Detection, Github. Available at: https://github.com/MaybeShewill-CV/lanenet-lane-detection.Google Scholar
Narote, S.P., Bhujbal, P.N., Narote, A.S. and Dhane, D.M. (2018). A review of recent advances in lane detection and departure warning system. Pattern Recognition, 73, 216–234.CrossRefGoogle Scholar
Neven, D., et al. (2018). Towards End-to-End Lane Detection: An Instance Segmentation Approach. In 2018 IEEE Intelligent Vehicles Symposium (IV). IEEE, pp. 286–291, Changshu, China.CrossRefGoogle Scholar
Nie, Z., Zhou, P., Liu, F., Wang, Z. and Gao, Y. (2019). Evaluation of orbit, clock and ionospheric corrections from five currently available SBAS L1 services: methodology and analysis. Remote Sensing, 11(4), 411.CrossRefGoogle Scholar
Nie, Z., Liu, F. and Gao, Y. (2020). Real-time precise point positioning with a low-cost dual-frequency GNSS device. GPS Solutions, 24(1), 9.CrossRefGoogle Scholar
Odolinski, R. and Teunissen, P. J. G. (2017). Low-cost, high-precision, single-frequency GPS–BDS RTK positioning. GPS Solutions, 21(3), 1315–1330.CrossRefGoogle Scholar
Odolinski, R. and Teunissen, P. J. G. (2018). An assessment of smartphone and low-cost multi-GNSS single-frequency RTK positioning for low, medium and high ionospheric disturbance periods. Journal of Geodesy, 93(5), 701–722.CrossRefGoogle Scholar
Pan, X., Shi, J., Luo, P., Wang, X. and Tang, X. (2018). Spatial as Deep: Spatial CNN for Traffic Scene Understanding. In Thirty-Second AAAI Conference on, New Orleans, Louisiana.Google Scholar
Paszke, A., Chaurasia, A., Kim, S. and Culurciello, E. (2016). Enet: A deep neural network architecture for real-time semantic segmentation. arXiv preprint arXiv:1606.02147.Google Scholar
Profound Positioning Inc. (2019). Profound-IP3. Available at: http://www.profoundpositioning.com/.Google Scholar
Quddus, M. (2006). High Integrity Map Matching Algorithms for Advanced Transport Telematics Applications. Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College, London UK. Citeseer, p. 270.Google Scholar
Saastamoinen, J. (1972). Atmospheric correction for the troposphere and stratosphere in radio ranging satellites. In Henriksen, S., Mancini, A. and Chovitz, B. (eds) The Use of Artificial. Washington DC, American Geophysical Union, 15, pp. 247–251.Google Scholar
Schaer, S. (1997). How to Use CODE's Global Ionosphere Maps. Hochschulstrasse, Switzerland, Astronomical Institute, University of Berne, pp. 1–9.Google Scholar
Schaer, S., Gurtner, W. and Feltens, J. (1998). IONEX: The Ionosphere Map Exchange Format Version 1. In Proceedings of the IGS AC Workshop, Darmstadt, Germany.Google Scholar
Schmid, R., Steigenberger, P., Gendt, G., Ge, M. and Rothacher, M. (2007). Generation of a consistent absolute phase-center correction model for GPS receiver and satellite antennas. Journal of Geodesy, 81(12), 781–798.CrossRefGoogle Scholar
Tian, Y., Ge, M. and Neitzel, F. (2015). Particle filter-based estimation of inter-frequency phase bias for real-time GLONASS integer ambiguity resolution. Journal of Geodesy, 89(11), 1145–1158.CrossRefGoogle Scholar
TuSimple. (2017). tusimple-benchmark, GitHub. Available at: https://github.com/TuSimple/tusimple-benchmark/wiki.Google Scholar
Welch, G. and Bishop, G. (1995). An Introduction to the Kalman Filter. Technical Report, Department of Computer Science, University of North Carolina at Chapel Hill.Google Scholar
Wu, J.T., Wu, S.C., Hajj, G.A., Bertiger, W.I. and Lichten, S.M. (1993). Effects of antenna orientation on GPS carrier phase. Manuscripta Geodaetica, 18(2), 91–98.Google Scholar
Zou, Q., Jiang, H., Dai, Q., Yue, Y., Chen, L. and Wang, Q. (2019). Robust lane detection from continuous driving scenes using deep neural networks. arXiv preprint arXiv:1903.02193.Google Scholar