Collaborative GNSS Positioning with the Aids of 3D City Models

Zhang, Guohao; Wen, Weisong; Hsu, Li‐Ta

doi:10.33012/2018.15830

Cited by 3 publications

(1 citation statement)

References 13 publications

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“…The shadow matching [6], one of the popular 3DMA GNSS techniques improving positioning accuracy by the building-model-predicted satellite visibility, has been extended with CP to reduce its solution ambiguous along a particular direction [7]. On the other hand, the satellite visibility prediction during shadow matching can be shared with neighboring users to exclude unreliable measurements [8]. Recently, another 3DMA GNSS method, GNSS ray-tracing [9], has been proposed to integrate with the DD technique for CP in the urban environment [10].…”

Section: Introductionmentioning

confidence: 99%

Scalability and Latency Analysis of the Centralized 3D Mapping Aided GNSS-Based Collaborative Positioning

Zhang

Hsu

et al. 2021

Proceedings of the 2021 International Technical Meeting of the Institute of Navigation

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Guohao Zhang received the bachelor's degree in mechanical engineering and automation from University of Science and Technology Beijing, China, in 2015. He received the master's degree in mechanical engineering and currently pursuing the Ph.D. degree in the Hong Kong Polytechnic University. His research interests including GNSS urban localization, cooperative positioning and multi-sensor integrated navigation.

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Section: Introductionmentioning

confidence: 99%

Scalability and Latency Analysis of the Centralized 3D Mapping Aided GNSS-Based Collaborative Positioning

Zhang

Hsu

et al. 2021

Proceedings of the 2021 International Technical Meeting of the Institute of Navigation

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3D Mapping Database Aided GNSS Based Collaborative Positioning Using Factor Graph Optimization

Zhang

Wen

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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The recent development in vehicle-to-everything (V2X) communication opens a new opportunity to improve the positioning performance of the road users. We explore the benefit of connecting the raw data of the global navigation satellite system (GNSS) from the agents. In urban areas, GNSS positioning is highly degraded due to signal blockage and reflection. 3D building model can play a major role in mitigating the GNSS multipath and non-line-of-sight (NLOS) effects. To combine the benefits of 3D models and V2X, we propose a novel 3D mapping aided (3DMA) GNSS-based collaborative positioning method that makes use of the available surrounding GNSS receivers' measurements. By complementarily integrating the ray-tracing based 3DMA GNSS and the double difference technique, the random errors (such as multipath and NLOS) are mitigated while eliminating the systematic errors (such as atmospheric delay and satellite clock/orbit biases) between road user. To improve the accuracy and robustness of the collaborative algorithm, factor graph optimization (FGO) is employed to optimize the positioning solutions among agents. Multiple low-cost GNSS receivers are used to collect both static and dynamic data in Hong Kong and to evaluate the proposed algorithm by post-processing. We reduce the GNSS positioning error from over 30 meters to less than 10 meters for road users in a deep urban canyon.

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Collaborative Navigation Simulation Tool Using Kalman Filter With Implicit Constraints

Garcia-Fernandez

Alkhatib

Schön

2019

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

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<p><strong>Abstract.</strong> Collaborative Positioning (CP) is a networked positioning technique in which different multi-sensor systems (nodes) enhance the accuracy and precision of these navigation solutions by performing measurements or by sharing information (links) between each other. The wide spectrum of available sensors that are used in these complex scenarios bring the necessity to analyze the sensibility of the system to different configurations in order to find optimal solutions. In this paper, we discuss the implementation and evaluation of a simulation tool that allows us to study these questions. The simulation tool is successfully implemented as a plane based localization problem, in which the sensor measurements are fused in a Collaborative Extended Kalman Filter (C-EKF) algorithm with implicit constraints. Using a real urban scenario with three vehicles equipped with various positioning sensors, the impact of the sensor configuration is investigated and discussed by intensive Monte Carlo simulations. The results show the influence of the laser scanner measurements on the accuracy and precision of the estimation, and the increased performance of the collaborative navigation techniques with respect to the single vehicle method.</p>

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Collaborative GNSS Positioning with the Aids of 3D City Models

Cited by 3 publications

References 13 publications

Scalability and Latency Analysis of the Centralized 3D Mapping Aided GNSS-Based Collaborative Positioning

Scalability and Latency Analysis of the Centralized 3D Mapping Aided GNSS-Based Collaborative Positioning

3D Mapping Database Aided GNSS Based Collaborative Positioning Using Factor Graph Optimization

Collaborative Navigation Simulation Tool Using Kalman Filter With Implicit Constraints

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