Improved Localization for Android Smartphones Based on Integration of Raw GNSS Measurements and IMU Sensors

Sheta, Ahmed N.; Mohsen, Ahmed; Sheta, Bassem I.; Hassan, Mohamed K.

doi:10.1109/comapp.2018.8460352

Cited by 10 publications

(7 citation statements)

References 3 publications

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“…The IMU sensors can be integrated with the GNSS observables to achieve a better localization solution. Sheta et al (2018) employed the raw GNSS measurements and the inertial sensors data from the smartphones to improve positioning solution. They used the Huawei mate 8 as a testing platform and investigated the accuracy of inertial sensors only solution and loosely coupled GPS/INS integration solution.…”

Section: Gnss/ins Integrationmentioning

confidence: 99%

GNSS smartphones positioning: advances, challenges, opportunities, and future perspectives

2021

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Starting from 2016, the raw Global Navigation Satellite System (GNSS) measurements can be extracted from the Android Nougat (or later) operating systems. Since then, GNSS smartphone positioning has been given much attention. A high number of related publications indicates the importance of the research in this field, as it has been doing in recent years. Due to the cost-effectiveness of the GNSS smartphones, they can be employed in a wide variety of applications such as cadastral surveys, mapping surveying applications, vehicle and pedestrian navigation and etc. However, there are still some challenges regarding the noisy smartphone GNSS observations, the environment effect and smartphone holding modes and the algorithm development part which restrict the users to achieve high-precision smartphone positioning. In this review paper, we overview the research works carried out in this field with a focus on the following aspects: first, to provide a review of fundamental work on raw smartphone observations and quality assessment of GNSS observations from major smart devices including Google Pixel 4, Google Pixel 5, Xiaomi Mi 8 and Samsung Ultra S20 in terms of their signal strengths and carrier-phase continuities, second, to describe the current state of smartphone positioning research field until most recently in 2021 and, last, to summarize major challenges and opportunities in this filed. Finally, the paper is concluded with some remarks as well as future research perspectives.

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Section: Gnss/ins Integrationmentioning

confidence: 99%

GNSS smartphones positioning: advances, challenges, opportunities, and future perspectives

2021

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“…Inter-agent relative distances are typically exploited to support collaborative strategies by means of exteroceptive sensors (e.g., Ultra-Wide Band (UWB), Light Detection And Ranging (LiDAR)). Thanks to the recent disclosure of GNSS raw measurements in the mass market GNSS receivers (such as for the embedded chipsets in the smartphones), developers, and researchers can now access raw GNSS observables (e.g., code/phase raw pseudorange measurements and Doppler measurements) prior to their actual use in PVT computation [40][41][42]. Such data can be transmitted, synchronised, and combined among different asynchronous users to reciprocally improve the positioning performance, the mutual situational awareness, and to open a variety of new solutions for the monitoring of GNSS integrity within a network of users [43,44].…”

Section: Dgnss-based Cooperative Positioningmentioning

confidence: 99%

A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities: The HANSEL Project

et al. 2020

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In urban contexts, the increasing density of electronic devices equipped with Global Navigation Satellite System (GNSS) receivers and complementary positioning technologies is attracting research and development efforts devoted to an improvement of the quality of life towards the smart city paradigm. Vehicular and pedestrian positioning and navigation capabilities are among the major drivers for innovation in this process. Ultra-low-cost electronics such as smartphones and Internet of Things (IoT) sensors aim at providing accurate and reliable positioning solutions through a set of promising solutions. Among these, snapshot positioning allows to remotely perform the post-processing of GNSS signals in IoT sensor networks while Wi-Fi™ ranging and cooperative positioning provide auxiliary anchors of opportunity to enhance indoor/outdoor positioning capabilities. This paper presents an innovative platform to perform a centralised testing and assessment of such positioning and navigation technologies along with a set of results obtained in the context of the European project HANSEL, by relying on current network technologies and infrastructures (i.e., Wi-Fi™ and cellular connectivity).

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“…Of course, inertial sensors can also be coupled with GNSS, e.g. Hide et al (2009) and Sheta et al (2018), cellular signals or visual sensors, e.g. Angelino et al (2012), Solin et al (2018) and Qin et al (2018), to improve the rotation measurements, but only provided that the device is in motion.…”

Section: Related Workmentioning

confidence: 99%

Investigation on Multi-Sensor Fusion Strategies for Improved Orientation Determination in Mobile Phone Imaging Applications

Elias

Maas

2020

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

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Abstract. Thanks to the rapid technological progress in the field of mobile devices, smartphones are increasingly becoming valuable for science. They can serve as photogrammetric measurement devices with built-in cameras, micro-electro-mechanical systems for orientation and position assessment, as well as powerful processing units allowing field-based data acquisition and processing. This paper outlines a comprehensive investigation focusing on the accuracy and stability of smartphone camera rotation parameters determined by built-in smartphone sensors. For that purpose, the rotation parameters were measured under a range of different conditions. Four test scenarios were defined considering indoor- and outdoor measurements using three different devices being in static and dynamic modes. Furthermore, the influence of magnetic perturbations was investigated. The rotation parameters were determined from the measurements applying different sensor fusion approaches. Reference values for accuracy assessment were provided by a superior precision inertial measurement unit that measured the rotation parameters simultaneously to the smartphone in each experiment. The analysis of the smartphone-based rotation parameters, separated in the Euler angles azimuth, pitch and roll, shows average accuracies below 2° for pitch and roll. In comparison, azimuth shows significantly lower accuracies of more than 30° especially when the smartphone is in motion and when it is exposed to magnetic perturbations. In this regard, advanced multi-sensor fusion approaches were examined that handle such interferences to considerably improve the accuracy of azimuth measurements. In conclusion, a summary of accuracies and stabilities to be expected from smartphone sensors is given referring to ambient conditions and investigated sensor fusion strategies.

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Improved Localization for Android Smartphones Based on Integration of Raw GNSS Measurements and IMU Sensors

Cited by 10 publications

References 3 publications

GNSS smartphones positioning: advances, challenges, opportunities, and future perspectives

GNSS smartphones positioning: advances, challenges, opportunities, and future perspectives

A Testbed for GNSS-Based Positioning and Navigation Technologies in Smart Cities: The HANSEL Project

Investigation on Multi-Sensor Fusion Strategies for Improved Orientation Determination in Mobile Phone Imaging Applications

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