GPS accuracy improvement by satellite selection using omnidirectional infrared camera

Meguro, Junichi; Murata, T.; Takiguchi, Jun-ichi; Amano, Yoshiharu; Hashizume, Takumi

doi:10.1109/iros.2008.4650709

Cited by 13 publications

(6 citation statements)

References 7 publications

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“…Recently, environment observation and modeling approaches have been used to mitigate multipath and NLOS errors in the GPS measurements and improve accuracy and availability in urban environments. Initial approaches applied upwards‐facing omnidirectional cameras to characterize the city skyline surrounding a GPS receiver's antenna to reject occluded signals . Zribi et al combined GPS measurements and odometric data with a digital road map to improve 2D vehicle positioning .…”

Section: Related Workmentioning

confidence: 99%

Probabilistic graphical fusion of LiDAR, GPS, and 3D building maps for urban UAV navigation

Chen

Gao

2019

NAVIGATION

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Because of the recent interest in unmanned air vehicle (UAV) commercialization, there is a great need for navigation algorithms that provide accurate and robust positioning in urban environments that are often Global Positioning Systems (GPS) challenged or denied. In this paper, we present a probabilistic graph-based navigation algorithm resilient to GPS errors. Fusing GPS pseudorange and Light Detection and Ranging (LiDAR) odometry measurements with 3D building maps, we apply a batch estimation approach to generate a robust trajectory estimate and maps of the surrounding environment. We then leverage the maps to locate potential sources of GPS multipath and mitigate the effects of degraded pseudorange measurements on the trajectory estimate. We experimentally validate our results with flight tests conducted in GPS-challenged and GPS-denied environments.

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Section: Related Workmentioning

confidence: 99%

Probabilistic graphical fusion of LiDAR, GPS, and 3D building maps for urban UAV navigation

Chen

Gao

2019

NAVIGATION

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“…The NLOS mitigation method used in this paper consists of detecting and rejecting the obstructed satellites that are the principal sources of the multipath and NLOS errors. Our method uses a sky-pointing camera to acquire the GNSS receiver antenna’s surrounding environment [ 14 , 31 , 58 ]. The captured images need to be segmented into sky and non-sky.…”

Section: Camera-based Non-line-of-sight Effect Mitigationmentioning

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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“…For example; high accuracy models were used to mitigate multipath in a survey area, improving the navigation solution [27] by minimising the number of false signals reaching the receiver [28]. Fisheye and IR cameras can be used to remove non-visible satellites in the position calculation, through post-processing or even in real-time [29,30] and these methods are independent of city models. When detailed obstruction models are employed, they are created through simulation [31] or by survey [32].…”

Section: Proposed Substitution Of 2d Vector Files and Basic 3d Datamentioning

confidence: 99%

Combining 2D Mapping and Low Density Elevation Data in a GIS for GNSS Shadow Prediction

Cahalane

2015

IJGI

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The number of satellites visible to a Global Navigation Satellite System (GNSS) receiver is important for high accuracy surveys. To aid with this, there are software packages capable of predicting GNSS visibility at any location of the globe at any time of day. These prediction packages operate by using regularly updated almanacs containing positional data for all navigation satellites; however, one issue that restricts their use is that most packages assume that there are no obstructions on the horizon. In an attempt to improve this, certain planning packages are now capable of modelling simple obstructions whereby portions of the horizon visible from one location can be blocked out, thereby simulating buildings or other vertical structures. While this is useful for static surveys, it is not applicable for dynamic surveys when the GNSS receiver is in motion. This problem has been tackled in the past by using detailed, high-accuracy building models and designing novel methods for modelling satellite positions using GNSS almanacs, which is a time-consuming and costly approach. The solution proposed in this paper is to use a GIS to combine existing, freely available GNSS prediction software to predict pseudo satellite locations, incorporate a 2.5D model of the buildings in an area created with national mapping agency 2D vector mapping and low density elevation data to minimise the need for a full survey, thereby providing savings in terms of cost and time. Following this, the ESRI ArcMap viewshed tool was used to ascertain what areas exhibit poor GNSS visibility due to obstructions over a wide area, and an accuracy assessment of the procedure was made.

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GPS accuracy improvement by satellite selection using omnidirectional infrared camera

Cited by 13 publications

References 7 publications

Probabilistic graphical fusion of LiDAR, GPS, and 3D building maps for urban UAV navigation

Probabilistic graphical fusion of LiDAR, GPS, and 3D building maps for urban UAV navigation

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

Combining 2D Mapping and Low Density Elevation Data in a GIS for GNSS Shadow Prediction

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