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Non-Dimensional Star-Identification
Open AccessLetter

An Evaluation of Low-Cost Vision Processors for Efficient Star Identification

1
Intel Corporation, Intel R&D Ireland Ltd, Collinstown, Collinstown Industrial Park, Co., W23 CX68 Kildare, Ireland
2
Ubotica Technologies, DCU Alpha, Old Finglas Road, Glasnevin, D11KXN4 Dublin, Ireland
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(21), 6250; https://doi.org/10.3390/s20216250
Received: 28 September 2020 / Revised: 22 October 2020 / Accepted: 28 October 2020 / Published: 2 November 2020
(This article belongs to the Special Issue Attitude Sensors)
Star trackers are navigation sensors that are used for attitude determination of a satellite relative to certain stars. A star tracker is required to be accurate and also consume as little power as possible in order to be used in small satellites. While traditional approaches use lookup tables for identifying stars, the latest advances in star tracking use neural networks for automatic star identification. This manuscript evaluates two low-cost processors capable of running a star identification neural network, the Intel Movidius Myriad 2 Vision Processing Unit (VPU) and the STM32 Microcontroller. The intention of this manuscript is to compare the accuracy and power usage to evaluate the suitability of each device for use in a star tracker. The Myriad 2 VPU and the STM32 Microcontroller have been specifically chosen because of their performance on computer vision algorithms alongside being cost-effective and low power consuming devices. The experimental results showed that the Myriad 2 proved to be efficient and consumed around 1 Watt of power while maintaining 99.08% accuracy with an input including false stars. Comparatively the STM32 was able to deliver comparable accuracy (99.07%) and power measurement results. The proposed experimental setup is beneficial for small spacecraft missions that require low-cost and low power consuming star trackers. View Full-Text
Keywords: power measurement; Myriad 2; STM32; OpenMV; star identification; star tracker; deep learning; star feature extraction power measurement; Myriad 2; STM32; OpenMV; star identification; star tracker; deep learning; star feature extraction
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MDPI and ACS Style

Agarwal, S.; Hervas-Martin, E.; Byrne, J.; Dunne, A.; Luis Espinosa-Aranda, J.; Rijlaarsdam, D. An Evaluation of Low-Cost Vision Processors for Efficient Star Identification. Sensors 2020, 20, 6250. https://doi.org/10.3390/s20216250

AMA Style

Agarwal S, Hervas-Martin E, Byrne J, Dunne A, Luis Espinosa-Aranda J, Rijlaarsdam D. An Evaluation of Low-Cost Vision Processors for Efficient Star Identification. Sensors. 2020; 20(21):6250. https://doi.org/10.3390/s20216250

Chicago/Turabian Style

Agarwal, Surabhi; Hervas-Martin, Elena; Byrne, Jonathan; Dunne, Aubrey; Luis Espinosa-Aranda, Jose; Rijlaarsdam, David. 2020. "An Evaluation of Low-Cost Vision Processors for Efficient Star Identification" Sensors 20, no. 21: 6250. https://doi.org/10.3390/s20216250

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Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

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