FAST CONVERGING LIDAR-AIDED PRECISE POINT POSITIONING: A CASE STUDY WITH LOW-COST GNSS

With the growing interest in autonomous driving, accurate vehicle positioning remains an open problem, especially in urban environments. According to regulatory organisations, the vehicle positioning accuracy is required to be centimetre-level. As the most used positioning technique which provides g...

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Main Authors: J. Zhang, K. Khoshelham, A. Khodabandeh
Format: Article
Language:English
Published: Copernicus Publications 2023-12-01
Series:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Online Access:https://isprs-annals.copernicus.org/articles/X-1-W1-2023/687/2023/isprs-annals-X-1-W1-2023-687-2023.pdf
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author J. Zhang
K. Khoshelham
A. Khodabandeh
author_facet J. Zhang
K. Khoshelham
A. Khodabandeh
author_sort J. Zhang
collection DOAJ
description With the growing interest in autonomous driving, accurate vehicle positioning remains an open problem, especially in urban environments. According to regulatory organisations, the vehicle positioning accuracy is required to be centimetre-level. As the most used positioning technique which provides globally referenced positioning solutions, GNSS is the fundamental component for realising real-time vehicle positioning, usually through the RTK approach. However, RTK requires a nearby reference station to enable integer ambiguity resolution for the ultra-precise carrier phase observations. In comparison, PPP makes use of State-Space Representation (SSR) corrections produced by global networks for satellite orbits and clocks to facilitate phase-based positioning. Moreover, IGS now offers Real-Time Service (RTS) to transmit such corrections. Notably, the major drawback of PPP is that it takes a long time to converge to precise solutions due to the carrier phase ambiguities being real-valued, which can be severely elongated when real-time corrections and low-cost GNSS receivers are used. In this paper, a tightly coupled positioning method is proposed, which shortens RT-PPP convergence to seconds by using lidar measurements referenced from an HD map through deep learning. The lidar measurements are generated by point cloud registration and weighted by their intensity values and geometric distributions, and are then combined with RT-PPP in an Extended Kalman-Filter (EKF), thus achieving fast convergence. Experimental results show that the proposed method achieves and maintains centimetre-level accuracy within 2 seconds using a low-cost UBLOX F9P receiver, which is a significant improvement as compared to the decimetre-level accuracy obtained from standalone RT-PPP.
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spelling doaj.art-095c4aa07f7548728878c2d9a0fb89272023-12-06T03:28:08ZengCopernicus PublicationsISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences2194-90422194-90502023-12-01X-1-W1-202368769410.5194/isprs-annals-X-1-W1-2023-687-2023FAST CONVERGING LIDAR-AIDED PRECISE POINT POSITIONING: A CASE STUDY WITH LOW-COST GNSSJ. Zhang0K. Khoshelham1A. Khodabandeh2Department of Infrastructure Engineering, The University of Melbourne, Parkville, Victoria, AustraliaDepartment of Infrastructure Engineering, The University of Melbourne, Parkville, Victoria, AustraliaDepartment of Infrastructure Engineering, The University of Melbourne, Parkville, Victoria, AustraliaWith the growing interest in autonomous driving, accurate vehicle positioning remains an open problem, especially in urban environments. According to regulatory organisations, the vehicle positioning accuracy is required to be centimetre-level. As the most used positioning technique which provides globally referenced positioning solutions, GNSS is the fundamental component for realising real-time vehicle positioning, usually through the RTK approach. However, RTK requires a nearby reference station to enable integer ambiguity resolution for the ultra-precise carrier phase observations. In comparison, PPP makes use of State-Space Representation (SSR) corrections produced by global networks for satellite orbits and clocks to facilitate phase-based positioning. Moreover, IGS now offers Real-Time Service (RTS) to transmit such corrections. Notably, the major drawback of PPP is that it takes a long time to converge to precise solutions due to the carrier phase ambiguities being real-valued, which can be severely elongated when real-time corrections and low-cost GNSS receivers are used. In this paper, a tightly coupled positioning method is proposed, which shortens RT-PPP convergence to seconds by using lidar measurements referenced from an HD map through deep learning. The lidar measurements are generated by point cloud registration and weighted by their intensity values and geometric distributions, and are then combined with RT-PPP in an Extended Kalman-Filter (EKF), thus achieving fast convergence. Experimental results show that the proposed method achieves and maintains centimetre-level accuracy within 2 seconds using a low-cost UBLOX F9P receiver, which is a significant improvement as compared to the decimetre-level accuracy obtained from standalone RT-PPP.https://isprs-annals.copernicus.org/articles/X-1-W1-2023/687/2023/isprs-annals-X-1-W1-2023-687-2023.pdf
spellingShingle J. Zhang
K. Khoshelham
A. Khodabandeh
FAST CONVERGING LIDAR-AIDED PRECISE POINT POSITIONING: A CASE STUDY WITH LOW-COST GNSS
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
title FAST CONVERGING LIDAR-AIDED PRECISE POINT POSITIONING: A CASE STUDY WITH LOW-COST GNSS
title_full FAST CONVERGING LIDAR-AIDED PRECISE POINT POSITIONING: A CASE STUDY WITH LOW-COST GNSS
title_fullStr FAST CONVERGING LIDAR-AIDED PRECISE POINT POSITIONING: A CASE STUDY WITH LOW-COST GNSS
title_full_unstemmed FAST CONVERGING LIDAR-AIDED PRECISE POINT POSITIONING: A CASE STUDY WITH LOW-COST GNSS
title_short FAST CONVERGING LIDAR-AIDED PRECISE POINT POSITIONING: A CASE STUDY WITH LOW-COST GNSS
title_sort fast converging lidar aided precise point positioning a case study with low cost gnss
url https://isprs-annals.copernicus.org/articles/X-1-W1-2023/687/2023/isprs-annals-X-1-W1-2023-687-2023.pdf
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