Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons

Bai, Xiwei; Wen, Weisong; Hsu, Li‐Ta

doi:10.1049/iet-its.2019.0587

Cited by 33 publications

(21 citation statements)

References 35 publications

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“…The resolution of elevation and azimuth angle information are identical to the precomputed skymask in Section 2.1 . For a given position in pixel, the calculation to convert into azimuth and elevation angle is the same approach described in [ 3 ]. Assuming the optical center of the camera is zenith pointing, each pixel inside the sky view image will be converted to a corresponding azimuth and elevation angle.…”

Section: Proposed Skymask Matching Methodsmentioning

confidence: 99%

“…This accuracy, however, suffers in dense urban areas because buildings block, reflect, and diffract the signals. These cause errors in satellite positioning and reduces accuracy and, in severe cases, the position error could exceed 50 m [ 2 , 3 ]. An improvement in the real time-positioning accuracy of low-cost GNSS systems in dense urban areas to within 5 m would benefit many different potential applications [ 4 ], such as cloud-sourced mobile mapping and object tracking.…”

Section: Introductionmentioning

confidence: 99%

“…When used in conjunction with image processing algorithms [ 2 , 4 , 14 , 15 , 16 , 17 ], they allow the exclusion of NLOS satellites from position calculations, improving positioning accuracy [ 15 ]. Furthermore, research indicates that if both LiDAR and fisheye cameras are used in conjunction, positioning accuracy in urban areas can be further enhanced [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

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Skymask Matching Aided Positioning Using Sky-Pointing Fisheye Camera and 3D City Models in Urban Canyons

Lee

et al. 2020

Sensors

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3D-mapping-aided (3DMA) global navigation satellite system (GNSS) positioning that improves positioning performance in dense urban areas has been under development in recent years, but it still faces many challenges. This paper details a new algorithm that explores the potential of using building boundaries for positioning and heading estimation. Rather than applying complex simulations to analyze and correct signal reflections by buildings, the approach utilizes a convolutional neural network to differentiate between the sky and building in a sky-pointing fisheye image. A new skymask matching algorithm is then proposed to match the segmented fisheye images with skymasks generated from a 3D building model. Each matched skymask holds a latitude, longitude coordinate and heading angle to determine the precise location of the fisheye image. The results are then compared with the smartphone GNSS and advanced 3DMA GNSS positioning methods. The proposed method provides degree-level heading accuracy, and improved positioning accuracy similar to other advanced 3DMA GNSS positioning methods in a rich urban environment.

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Section: Proposed Skymask Matching Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Skymask Matching Aided Positioning Using Sky-Pointing Fisheye Camera and 3D City Models in Urban Canyons

Lee

et al. 2020

Sensors

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“…There are many proposed methods to detect LOS+ NLOS or NLOS-only signals. For example, some special antennas, such as dual-polarized antenna or array antenna, a 3D city model, a sky-pointing camera, and several algorithm-based methods based on GNSS measurements could be used to detect LOS+NLOS or NLOS-only signals [35][36][37][38][39]. In this study, we performed the GPS multipath detection (i.e., both LOS+NLOS and NLOSonly detection) based on the carrier-to-noise-density ratio (C/N 0 ) measurements through a dual-polarized antenna, which was proposed in [35].…”

Section: Introductionmentioning

confidence: 99%

GPS Multipath Detection Based on Carrier-to-Noise-Density Ratio Measurements from a Dual-Polarized Antenna

Kim¹,

Lee²,

Park³

2021

Preprint

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In this study, the global positioning system (GPS) multipath detection was performed based on the carrier-tonoise-density ratio, C/N 0 , measured through a dual-polarized antenna. As the right hand circular polarization (RHCP) antenna is sensitive to the signals directly received from the GPS, and the left hand circular polarization (LHCP) antenna is sensitive to the singly reflected signals, the C/N 0 difference between the RHCP and LHCP measurements is used for multipath detection. Once we collected the GPS signals in a low multipath location, we calculated the C/N 0 difference to obtain a threshold value that can be used to detect the multipath GPS signal received from another location. The results were validated through a ray-tracing simulation.

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“…Besides visual navigation with cameras, global navigation satellite system (GNSS) receivers are alternative sensors for autonomous systems in outdoor environments. However, GNSS measurements strongly deteriorate in urban canyons due to signal blockage and reflections, which can result in positioning errors up to 100 m in dense urban areas [17]. Thus, GNSS based navigation requires sophisticated approaches in urban areas and they often use additional sensors to correct the measurements (e.g., [17,18]).…”

Section: Introductionmentioning

confidence: 99%

On the Benefits of Color Information for Feature Matching in Outdoor Environments

Hoffmann

2020

Robotics

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The detection and description of features is one basic technique for many visual robot navigation systems in both indoor and outdoor environments. Matched features from two or more images are used to solve navigation problems, e.g., by establishing spatial relationships between different poses in which the robot captured the images. Feature detection and description is particularly challenging in outdoor environments, and widely used grayscale methods lead to high numbers of outliers. In this paper, we analyze the use of color information for keypoint detection and description. We consider grayscale and color-based detectors and descriptors, as well as combinations of them, and evaluate their matching performance. We demonstrate that the use of color information for feature detection and description markedly increases the matching performance.

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Using Sky‐pointing fish‐eye camera and LiDAR to aid GNSS single‐point positioning in urban canyons

Cited by 33 publications

References 35 publications

Skymask Matching Aided Positioning Using Sky-Pointing Fisheye Camera and 3D City Models in Urban Canyons

Skymask Matching Aided Positioning Using Sky-Pointing Fisheye Camera and 3D City Models in Urban Canyons

GPS Multipath Detection Based on Carrier-to-Noise-Density Ratio Measurements from a Dual-Polarized Antenna

On the Benefits of Color Information for Feature Matching in Outdoor Environments

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