Optical Calibration System

Subproject within the ADome project

Bachelor thesis (2021)

Authors

D. Roos Electrical Engineering, Mathematics and Computer Science
T.I. van Velden Electrical Engineering, Mathematics and Computer Science

Contributors

M. Spirito Electronics - EEMCS (supervisor 1)
F.A. Musters Electronics - EEMCS (supervisor 1)
R.A. Coesoij Electrical Engineering, Mathematics and Computer Science (supervisor 1)
L.C.N. de Vreede Electronics - EEMCS (supervisor 2)
M. Alonso Del Pino Tera-Hertz Sensing - EEMCS (supervisor 2)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Daan Roos, Tycho van Velden
Localization Computer Vision C++ Localization Accuracy Blob Detection OpenCV
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Published Date

30-06-2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

In the ADome project, a truncated icosahedron is used as a frame for multiple antenna nodes. These nodes are used to measure the radiation pattern of an antenna-under-test located in the center of this dome. Since these antennas can be placed anywhere in the ADome by the user, a calibration system is necessary to find the spherical coordinates of each of these antennas.

In this thesis, a method is proposed that uses computer vision algorithms, written using the OpenCV library in C++, to locate each of these antennas by detecting controllable LEDs attached to the PCBs of the antenna nodes. The found pixel locations will then be used to get the spherical coordinates of the antennas with respect to the camera. Finally these spherical coordinates will be transformed to fit to the coordinate system of the ADome itself, by detecting landmarks in the form of fiducial markers which are located at predetermined locations.

The methods of recognition of the antennas within using computer vision are discussed, implemented and tested on real and simulated data. The accuracy of finding the antennas on the real data was within 2 pixels from the true location.

The methods regarding estimating the location of the antenna with respect to the camera are discussed next. These methods include a distortion estimation and correction, after which each pixel will get there corresponding spherical angles θ and φ. These methods are also tested on simulated and real data, where the accuracy on the real data falls within 0.5 degrees in comparison to the true data.

Finally the methods of transforming the spherical angle θ and φ of the antenna with respect to the camera, to the coordinate system of the ADome are discussed. This starts with a method of recognizing fiducial landmarks (landmarks are accurately known reference points), where in this research there is chosen for ArUco fiducials. After this, the location of these landmarks can be used to determine the location and orientation of the camera within the ADome. The method of finding the distances between each of the landmarks and the camera, is discussed and tested and has an accuracy of 2 cm. The method of finding the location of the camera, and the method of finding the orientation of the camera are discussed, however this has not been tested and fully implemented yet. With this location and orientation the location of an antenna can be easily determined.

In conclusion, this thesis is a good starting point for designing an Optical Calibration System, which could make determining the location of each of the antennas faster, and more accurate.