GNSS Shadow Matching: Improving Urban Positioning Accuracy Using a 3D City Model with Optimized Visibility Scoring Scheme

Wang, Lei; Groves, Paul D.; Ziebart, Marek

doi:10.1002/navi.38

Cited by 137 publications

(110 citation statements)

References 18 publications

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“…For instance, they are used in estimating the visibility of a landmark [182,183], assessing façade visibility for city marketing [184,185], in determining the optimal location for surveillance cameras [186][187][188], sensor coverage assessment [189], improving road safety [190], assessing sniper hazards [191], and in real estate mass valuation in the urban areas, based on the assumption that the view from an apartment is one of the factors driving its price [192][193][194]. Further applications involve predicting the visibility of GNSS satellites in the built environment and mitigating the multipath effect [195][196][197][198][199][200][201][202][203][204]. Such methods are valuable for enhancing map matching for navigation in urban canyons [205].…”

Section: Visibility Analysismentioning

confidence: 99%

Applications of 3D City Models: State of the Art Review

Biljecki

Stoter

Ledoux

et al. 2015

IJGI

610

380

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In the last decades, 3D city models appear to have been predominantly used for visualisation; however, today they are being increasingly employed in a number of domains and for a large range of tasks beyond visualisation. In this paper, we seek to understand and document the state of the art regarding the utilisation of 3D city models across multiple domains based on a comprehensive literature study including hundreds of research papers, technical reports and online resources. A challenge in a study such as ours is that the ways in which 3D city models are used cannot be readily listed due to fuzziness, terminological ambiguity, unclear added-value of 3D geoinformation in some instances, and absence of technical information. To address this challenge, we delineate a hierarchical terminology (spatial operations, use cases, applications), and develop a theoretical reasoning to segment and categorise the diverse uses of 3D city models. Following this framework, we provide a list of identified use cases of 3D city models (with a description of each), and their applications. Our study demonstrates that 3D city models are employed in at least 29 use cases that are a part of more than 100 applications. The classified inventory could be useful for scientists as well as stakeholders in the geospatial industry, such as companies and national mapping agencies, as it may serve as a reference document to better position their operations, design product portfolios, and to better understand the market.

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Section: Visibility Analysismentioning

confidence: 99%

Applications of 3D City Models: State of the Art Review

Biljecki

Stoter

Ledoux

et al. 2015

IJGI

610

380

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“…Figure 5 shows an example. The elevation of the building boundary is computed at a range of azimuths as described in Wang et al (2013a) and Wang et al (2012). Building boundaries are computed over a grid of candidate user locations.…”

Section: S M a Rt P H O N E S H A D Ow M Atc H I N Gmentioning

confidence: 99%

Smartphone Shadow Matching for Better Cross-street GNSS Positioning in Urban Environments

2014

Self Cite

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Global Navigation Satellite System (GNSS) shadow matching is a new positioning technique that determines position by comparing the measured signal availability and strength with predictions made using a three-dimensional (3D) city model. It complements conventional GNSS positioning and can significantly improve cross-street positioning accuracy in dense urban environments. This paper describes how shadow matching has been adapted to work on an Android smartphone and presents the first comprehensive performance assessment of smartphone GNSS shadow matching. Using GPS and GLONASS data recorded at 20 locations within central London, it is shown that shadow matching significantly outperforms conventional GNSS positioning in the cross-street direction. The success rate for obtaining a cross-street position accuracy within 5 m, enabling the correct side of a street to be determined, was 54·50% using shadow matching, compared to 24·77% for the conventional GNSS position. The likely performance of four-constellation shadow matching is predicted, the feasibility of a large-scale implementation of shadow matching is assessed, and some methods for improving performance are proposed. A further contribution is a signal-tonoise ratio analysis of the direct line-of-sight and non-line-of-sight signals received on a smartphone in a dense urban environment. K E Y

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“…car or pedestrian navigation. The aim is to predict the satellite availability or detect possible signal deteriorations in order to improve the positioning solution on the meter level (Groves et al, 2012;Wang et al, 2013). Primarily the signal quality check serves for the mitigation of NLOS reception, since these effects have the potential to cause limitless errors in the range measurements (Strode and Groves, 2015).…”

Section: Related Workmentioning

confidence: 99%

Precise Positioning of Uavs – Dealing With Challenging RTK-GPS Measurement Conditions During Automated Uav Flights

Zimmermann

Eling

Klingbeil

et al. 2017

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

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ABSTRACT:For some years now, UAVs (unmanned aerial vehicles) are commonly used for different mobile mapping applications, such as in the fields of surveying, mining or archeology. To improve the efficiency of these applications an automation of the flight as well as the processing of the collected data is currently aimed at. One precondition for an automated mapping with UAVs is that the georeferencing is performed directly with cm-accuracies or better. Usually, a cm-accurate direct positioning of UAVs is based on an onboard multi-sensor system, which consists of an RTK-capable (real-time kinematic) GPS (global positioning system) receiver and additional sensors (e.g. inertial sensors). In this case, the absolute positioning accuracy essentially depends on the local GPS measurement conditions. Especially during mobile mapping applications in urban areas, these conditions can be very challenging, due to a satellite shadowing, non-line-of sight receptions, signal diffraction or multipath effects. In this paper, two straightforward and easy to implement strategies will be described and analyzed, which improve the direct positioning accuracies for UAV-based mapping and surveying applications under challenging GPS measurement conditions. Based on a 3D model of the surrounding buildings and vegetation in the area of interest, a GPS geometry map is determined, which can be integrated in the flight planning process, to avoid GPS challenging environments as far as possible. If these challenging environments cannot be avoided, the GPS positioning solution is improved by using obstruction adaptive elevation masks, to mitigate systematic GPS errors in the RTK-GPS positioning. Simulations and results of field tests demonstrate the profit of both strategies.

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GNSS Shadow Matching: Improving Urban Positioning Accuracy Using a 3D City Model with Optimized Visibility Scoring Scheme

Cited by 137 publications

References 18 publications

Applications of 3D City Models: State of the Art Review

Applications of 3D City Models: State of the Art Review

Smartphone Shadow Matching for Better Cross-street GNSS Positioning in Urban Environments

Precise Positioning of Uavs – Dealing With Challenging RTK-GPS Measurement Conditions During Automated Uav Flights

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