Star tracker for mobile applications

Marbel, Revital; Ben‐Moshe, Boaz; Yozevitch, Roi

doi:10.1109/icsee.2016.7806068

Cited by 1 publication

(2 citation statements)

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“…The algorithm proposed in this work copes with a certain level of lens distortion (as explained above) and relies on the sensor's ability to detect starlight automatically. Our experiment shows that this can be achieved in some of the new mobile devices' cameras; see [36]. The state-of-the-art algorithm Tetra [21], for the lost-in-space problem star-tracking problem, presents a fast identification algorithm (0.14 s per image) with a high success rate (≈95 %).…”

Section: Computing Efficiencymentioning

confidence: 87%

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Star-Tracker Algorithm for Smartphones and Commercial Micro-Drones

Marbel

Ben‐Moshe

Yozevitch

2020

Sensors

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This paper presents a star-tracking algorithm to determine the accurate global orientation of autonomous platforms such as nano satellites, U A V s, and micro-drones using commercial-off-the-shelf ( C O T S ) mobile devices such as smartphones. Such star-tracking is especially challenging because it is based on existing cameras which capture a partial view of the sky and should work continuously and autonomously. The novelty of the proposed framework lies both in the computational efficiency and the ability of the star-tracker algorithm to cope with noisy measurements and outliers using affordable C O T S mobile platforms. The presented algorithm was implemented and tested on several popular platforms including: Android mobile devices, commercial-micro drones, and Raspberry Pi. The expected accuracy of the reported orientation is [0.1°,0.5°].

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Section: Computing Efficiencymentioning

confidence: 87%

“…One of the goals of this work was to provide an empirical basis for research on star detection and tracking. To this end, we have collected hand-taken star frames from three types of smart-phones, a Raspberry Pi camera, and images from a time-lapse camera; see [36].…”

Section: Star Frames Data Setmentioning

confidence: 99%