3D building model-assisted snapshot positioning algorithm

Kumar, Rakesh; Petovello, Mark G.

doi:10.1007/s10291-017-0661-2

Cited by 9 publications

(5 citation statements)

References 22 publications

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“…3DMA GNSS ranging has also been combined with “direct positioning,” which uses the receiver correlator outputs to score an array of position hypothesis . A similar approach determines position by matching the predicted path delay(s) of the reflected component(s) at each candidate position with the outputs of a signal correlator bank …”

Section: Review Of 3d‐mapping–aided Gnssmentioning

confidence: 99%

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Performance assessment of 3D‐mapping–aided GNSS part 1: Algorithms, user equipment, and review

Groves

Adjrad

2019

NAVIGATION

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A full performance assessment of 3D‐mapping–aided (3DMA) GNSS in dense urban areas is presented. This first part of a two‐part paper focuses on the effects of algorithm design and user equipment, based on data collected in London using Leica Viva GS15 and u‐blox EVK‐M8T GNSS receivers and a Nexus 9 Android tablet. Best performance is obtained by combining shadow matching with likelihood‐based 3DMA GNSS ranging using hypothesis‐domain integration. Improved versions of the algorithms, together with a comprehensive tuning process, are described. Root mean square horizontal position errors obtained using data from Leica, u‐blox, and Nexus receivers are 3.5, 4.7, and 4.9 m, respectively, compared with 23.6, 26.4, and 31.0 m using conventional GNSS positioning, about a factor of six improvement. Optimal algorithm tuning depends on the environment and the impact of varying grid spacing of the candidate positions is assessed. Algorithms have also been shown to operate in real time on both a Raspberry Pi 3 and a Samsung Galaxy S8+ Android smartphone.

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Section: Review Of 3d‐mapping–aided Gnssmentioning

confidence: 99%

“…34 A similar approach determines position by matching the predicted path delay(s) of the reflected component(s) at each candidate position with the outputs of a signal correlator bank. 35…”

Section: D-mapping-aided Rangingmentioning

confidence: 99%

Performance assessment of 3D‐mapping–aided GNSS part 1: Algorithms, user equipment, and review

Groves

Adjrad

2019

NAVIGATION

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“…The travelled path and the buildings’ height in downtown Calgary are shown in Figure 14 . The buildings’ height is obtained from the commercially available 3D building model presented in [ 17 , 22 , 64 ]. Since the camera is pointed upwards, both the NLOS mitigation algorithm and the visual odometry are influenced by the building height.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…); (iii)

-based multipath detection (

-based selection and weighting [ 11 ], multi-frequency

-based multipath detection, etc. ); (iv) NLOS detection using a sky-pointing camera [ 12 , 13 , 14 ] and a 3D building model [ 15 , 16 , 17 ]. The research proposed in this paper uses a sky-pointing camera setup similar to what is used in [ 12 , 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas

Gakne

O’Keefe

2018

Sensors

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This paper presents a method of fusing the ego-motion of a robot or a land vehicle estimated from an upward-facing camera with Global Navigation Satellite System (GNSS) signals for navigation purposes in urban environments. A sky-pointing camera is mounted on the top of a car and synchronized with a GNSS receiver. The advantages of this configuration are two-fold: firstly, for the GNSS signals, the upward-facing camera will be used to classify the acquired images into sky and non-sky (also known as segmentation). A satellite falling into the non-sky areas (e.g., buildings, trees) will be rejected and not considered for the final position solution computation. Secondly, the sky-pointing camera (with a field of view of about 90 degrees) is helpful for urban area ego-motion estimation in the sense that it does not see most of the moving objects (e.g., pedestrians, cars) and thus is able to estimate the ego-motion with fewer outliers than is typical with a forward-facing camera. The GNSS and visual information systems are tightly-coupled in a Kalman filter for the final position solution. Experimental results demonstrate the ability of the system to provide satisfactory navigation solutions and better accuracy than the GNSS-only and the loosely-coupled GNSS/vision, 20 percent and 82 percent (in the worst case) respectively, in a deep urban canyon, even in conditions with fewer than four GNSS satellites.

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“…individual multipath propagation delays [127]. Using 3D city models to assist multipath detection, which is a combination of localization and mobile mapping, has also been researched [128]. Furthermore, some papers focus on estimating multipath parameters in real-time by using Simultaneous Localization And Mapping (SLAM) [129] and Extended KF (EKF) [130] algorithms.…”

Section: Table III Localization Error Sources and Mitigation Part #1:...mentioning

confidence: 99%

Toward Location-Enabled IoT (LE-IoT): IoT Positioning Techniques, Error Sources, and Error Mitigation

Zhuang

et al. 2021

IEEE Internet Things J.

131

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Localization techniques are becoming key to add location context to Internet of Things (IoT) data without human perception and intervention. Meanwhile, the newly-emerged Low-Power Wide-Area Network (LPWAN) and 5G technologies have become strong candidates for mass-market localization applications. However, various error sources have limited localization performance by using such IoT signals. This paper reviews the IoT localization system through the following sequence: IoT localization system review -localization data sourceslocalization algorithms -localization error sources and mitigation -localization performance evaluation. Compared to the related surveys, this paper has a more comprehensive and state-of-theart review on IoT localization methods, an original review on IoT localization error sources and mitigation, an original review on IoT localization performance evaluation, and a more comprehensive review of IoT localization applications, opportunities, and challenges. Thus, this survey provides comprehensive guidance for peers who are interested in enabling localization ability in the existing IoT systems, using IoT systems for localization, or integrating IoT signals with the existing localization sensors.

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3D building model-assisted snapshot positioning algorithm

Cited by 9 publications

References 22 publications

Performance assessment of 3D‐mapping–aided GNSS part 1: Algorithms, user equipment, and review

Performance assessment of 3D‐mapping–aided GNSS part 1: Algorithms, user equipment, and review

Tightly-Coupled GNSS/Vision Using a Sky-Pointing Camera for Vehicle Navigation in Urban Areas

Toward Location-Enabled IoT (LE-IoT): IoT Positioning Techniques, Error Sources, and Error Mitigation

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