Static and dynamic performance evaluation of low-cost RTK GPS receivers

Skoglund, Martin A.; Petig, Thomas; Vedder, Benjamin; Eriksson, Henrik; Schiller, Elad Michael

doi:10.1109/ivs.2016.7535357

Cited by 27 publications

(19 citation statements)

References 3 publications

(3 reference statements)

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“…t−1 ) follows from the mobility models (1) and (3). Based on these assumptions, and recalling that models are Gaussian and linear, the centralized ICP estimate reduces to the Kalman filter:…”

Section: Problem Formulation and System Modelmentioning

confidence: 99%

“…For positioning, in the rural area vehicles rely solely on GNSS, while in the urban canyon they can also use features, which are randomly deployed over the area. In this scenario, features are assumed to be static, thus their mobility model in (3) reduces to x (F)…”

Section: Performance Evaluationmentioning

confidence: 99%

“…Standard GNSS provide an accuracy of 5-10 meters in open sky areas, i.e., when there is a direct line-of-sight between the vehicle receiver and satellites [2]. Augmented by differential corrections and/or multi-constellation receivers, they can achieve meter-level accuracy in ideal operating conditions, or even centimeter level in Real Time Kinematics (RTK) variants [3]. However, RTK is still subject to long and unpredictable convergence times when cold starting.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Implicit Cooperative Positioning in Vehicular Networks

Soatti

Nicoli

Garcia

et al. 2018

IEEE Trans. Intell. Transport. Syst.

109

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Absolute positioning of vehicles is based on Global Navigation Satellite Systems (GNSS) combined with on-board sensors and high-resolution maps. In Cooperative Intelligent Transportation Systems (C-ITS), the positioning performance can be augmented by means of vehicular networks that enable vehicles to share location-related information. This paper presents an Implicit Cooperative Positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle (V2V) connectivity in an innovative manner, avoiding the use of explicit V2V measurements such as ranging. In the ICP approach, vehicles jointly localize non-cooperative physical features (such as people, traffic lights or inactive cars) in the surrounding areas, and use them as common noisy reference points to refine their location estimates. Information on sensed features are fused through V2V links by a consensus procedure, nested within a message passing algorithm, to enhance the vehicle localization accuracy. As positioning does not rely on explicit ranging information between vehicles, the proposed ICP method is amenable to implementation with off-the-shelf vehicular communication hardware. The localization algorithm is validated in different traffic scenarios, including a crossroad area with heterogeneous conditions in terms of feature density and V2V connectivity, as well as a real urban area by using Simulation of Urban MObility (SUMO) for traffic data generation. Performance results show that the proposed ICP method can significantly improve the vehicle location accuracy compared to the stand-alone GNSS, especially in harsh environments, such as in urban canyons, where the GNSS signal is highly degraded or denied.

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Section: Problem Formulation and System Modelmentioning

confidence: 99%

Section: Performance Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Implicit Cooperative Positioning in Vehicular Networks

Soatti

Nicoli

Garcia

et al. 2018

IEEE Trans. Intell. Transport. Syst.

109

View full text Add to dashboard Cite

show abstract

“…En [13] se hace una evaluación de dos sistemas GPS-RTK de bajo costo (Skytraq NS-RAW y Ublox M8N) analizando la performance de los mismos bajo una configuración estática y una dinámica. Además, para ambas configuraciones se comparan los resultados obtenidos con un sistema GPS-RTK de alta gama de dos bandas (L1 y L2).…”

Section: Trabajo Relacionadounclassified

Caracterización de un Sistema GPS RTK de Bajo Costo

Pistarelli

Pire

Kofman

2019

RTyC

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En las últimas décadas la comunidad robótica ha adoptado cada vez más el uso de sensores GPS-RTK para estimar de forma precisa la localización de robots móviles en ambientes exteriores. En este trabajo se presenta la caracterización de un sistema GPS-RTK de bajo costo. A partir del montaje de las unidades base y móvil se adquirieron datos de posición corregidos en tiempo real por el mismo sistema RTK bajo diferentes disposiciones físicas, velocidades de actualización y condiciones ambientales presentándose posteriormente un estudio estadístico de los resultados obtenidos.

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“…Low-cost single frequency Real-Time Kinematics (RTK) modules have recently been released by several manufacturers [1]. This type of receivers allows to obtain much better accuracy, reaching decimeter-level accuracy, than traditional low-cost receivers, thus opening the world of precise GNSS positioning to a new sector.…”

Section: Introductionmentioning

confidence: 99%

Estimation of the Base Station Position Error in a RTK Receiver Using State Augmentation in a Kalman Filter

Thevenon

Vezinet

Estrade

2018

2018 9th ESA Workshop on Satellite NavigationTechnologies and European Workshop on GNSS Signals and Signal Processing (NAVITEC)

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Low-cost single frequency Real-Time Kinematics (RTK) modules have recently been released by several manufacturers. This type of receivers allows to obtain much better accuracy, reaching decimeter-level accuracy, than traditional low-cost receivers, thus opening the world of precise GNSS positioning to a new sector. However, while this type of system will provide very good relative positioning accuracy, the absolute positioning accuracy might be degraded if the position of the RTK base station is not estimated with sufficient accuracy. Any bias on the RTK base station position will introduce the same bias on the RTK rover position. This paper proposes a modification to the position estimation algorithm that includes the real-time estimation of the RTK base station position error, by combining both the Single Point Positioning Solution and the RTK solution. The algorithm is illustrated using 2 types of real data: first, for a fixed reference station using GNSS observations only, then for a moving vehicle using a sensor fusion algorithm between GNSS, inertial and odometer observations. Performance analysis shows that the bias affecting the absolute position of the RTK rover can be estimated using the proposed algorithm, decreasing the horizontal bias from a few meters to a few decimeters.

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Static and dynamic performance evaluation of low-cost RTK GPS receivers

Cited by 27 publications

References 3 publications

Implicit Cooperative Positioning in Vehicular Networks

Implicit Cooperative Positioning in Vehicular Networks

Caracterización de un Sistema GPS RTK de Bajo Costo

Estimation of the Base Station Position Error in a RTK Receiver Using State Augmentation in a Kalman Filter

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