Centimeter-level positioning by instantaneous lidar-aided GNSS ambiguity resolution

Zhang, Junjie; Khodabandeh, Amir; Khoshelham, Kourosh

doi:10.1088/1361-6501/ac82dd

Cited by 8 publications

(1 citation statement)

References 40 publications

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“…Second, it has been utilised to enable integer ambiguity resolution in RTK. For instance, a lidar-aided instantaneous ambiguity resolution method was presented in (Zhang et al, 2022) which achieved the ambiguity success rate of 100% using single-system single-frequency observations. Finally, numerous studies have shown the effectiveness of lidar measurements for speeding up the convergence of PPP by successive scan matching (Li et al, 2021a, Li et al, 2021b, Li et al, 2022.…”

Section: Introductionmentioning

confidence: 99%

Fast Converging Lidar-Aided Precise Point Positioning: A Case Study With Low-Cost GNSS

Zhang,

Khoshelham,

Khodabandeh

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. 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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Section: Introductionmentioning

confidence: 99%

Fast Converging Lidar-Aided Precise Point Positioning: A Case Study With Low-Cost GNSS

Zhang,

Khoshelham,

Khodabandeh

2023

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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On the role of lidar measurements in speeding up precise point positioning convergence

2023

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Global navigation satellite system (GNSS) and light detection and ranging (lidar) are well known to be complementary for vehicle positioning in urban canyons, where GNSS observations are prone to signal blockage and multi-path. As one of the most common carrier-phase-based precise positioning techniques, precise point positioning (PPP) enables single-receiver positioning as it utilizes state-space representation corrections for satellite orbits and clocks and does not require a nearby reference station. Yet PPP suffers from a long positioning convergence time. In this contribution, we propose to reduce the PPP convergence using an observation-level integration of GNSS and lidar. Lidar measurements, in the form of 3D keypoints, are generated by registering online scans to a pre-built high-definition map through deep learning and are then combined with dual-frequency PPP (DF-PPP) observations in an extended Kalman filter implementing the constant-velocity model that captures the vehicle dynamics. We realize real-time PPP (RT-PPP) in this integration using the IGS real-time service products for vehicle positioning. Comprehensive analyses are provided to evaluate different combinations of measurements and PPP corrections in both static and simulated kinematic modes using data captured by multiple receivers. Experimental results show that the integration achieves cm-level accuracy and instantaneous convergence by using redundant measurements. Accordingly, for classical PPP accuracy of 10 cm and convergence within minutes, respectively, lidar input is only required once every 10 s.

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PPP based on factor graph optimization

Xiao,

Zhou

et al. 2024

Meas. Sci. Technol.

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Kalman or Kalman-related filtering methods are routinely applied in precise point positioning (PPP). However, in robot simultaneous localization and mapping (SLAM) systems, the factor graph optimization (FGO) has proved advantages over filtering methods in recent years, e.g., reducing the linearization errors and support of plug-and-play feature for multiple sensor fusion. Therefore, it would be interesting to apply the FGO to PPP. In addition, it will also facilitate the tight integration of PPP with Visual/LiDAR SLAM. In this work, PPP is solved under the factor graph optimization framework. A factor graph for PPP has been constructed. Results from 268 IGS-MGEX stations show that the factor graph optimization method can achieve a similar performance with that of Kalman filtering. First, the positioning accuracy in the convergence period can be improved for PPP based on factor graph optimization because it optimizes the entire state variables based on all the available observations. For applications that do not require real-time processing, the observation after the current states, e.g., future observations, can also be used to enhance the current state estimation. Second, the accuracy of static PPP is almost the same for the two methods with millimeter-accuracy for horizontal directions and centimeter-accuracy for vertical directions. Third, the kinematic PPP for both methods can achieve centimeter-level accuracy in horizontal directions and decimeter-level accuracy in vertical directions. Although the performance is comparable, it is noted that the computational efficiency of factor graph optimization method is still a problem. For each epoch, the average of elapsed time for Kalman filtering is 132 microseconds, while that of factor graph optimization method is 9664 microseconds. The elapsed time of factor graph optimization method can be further improved if the fix-window optimization technique is applied, which will be investigated in the future.

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Centimeter-level positioning by instantaneous lidar-aided GNSS ambiguity resolution

Cited by 8 publications

References 40 publications

Fast Converging Lidar-Aided Precise Point Positioning: A Case Study With Low-Cost GNSS

Fast Converging Lidar-Aided Precise Point Positioning: A Case Study With Low-Cost GNSS

On the role of lidar measurements in speeding up precise point positioning convergence

PPP based on factor graph optimization

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