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Tutorial for autonomous driving
Strašil, Vojtěch ; Zemčík, Tomáš (referee) ; Honec, Peter (advisor)
This thesis is devoted to the design of a teaching aid for autonomous driving intended primarily for second grade primary school students and secondary school students. The aim of this work was to create a robot that is able to move autonomously along a line or in a lane that is formed by two lines, based on data obtained from a camera sensor. The thesis deals with the design of the mechanical part of the robot, the control electronics and the software. The mechanical basis of the robot is formed by an RC car that has been modified with parts printed on a 3D printer. The control electronics is a Raspberry Pi version 4 single board computer, which is complemented by an Adeept Robot HAT. Most of the necessary peripherals are then connected to this extension. The operating software was written in Python and several use cases were created as part of the work. These examples include line recognition algorithms as well as a PSD controller.
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Determination of Motion Parameters in Machine Vision
Dušek, Stanislav ; Horák, Karel (referee) ; Janáková, Ilona (advisor)
This thesis describe about determination of camera motion parameters in plane. At first there are introduce the basics of motion tracking, is focused to find out displacement between two input images. Below is describe the algorithm GoodFeatruresToTrack, which find out the most significant point in a first image. The point is search out the good point, which will be easy to track in next image, reduce the data volume and prepare the input information (array of significant point) for the algorithm Lucas-Kanade optical flow. In second part is deal with processing and utilization estimations optical flow. There is median filtration, below is describe computation of homogenous transformation, which describe all affine transformation in affine space. As the result are coordinates, which describe the shift between the two input images as X-axis and Y-axis value. The project used the library Open Computer Vision.
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Tutorial for autonomous driving
Strašil, Vojtěch ; Zemčík, Tomáš (referee) ; Honec, Peter (advisor)
This thesis is devoted to the design of a teaching aid for autonomous driving intended primarily for second grade primary school students and secondary school students. The aim of this work was to create a robot that is able to move autonomously along a line or in a lane that is formed by two lines, based on data obtained from a camera sensor. The thesis deals with the design of the mechanical part of the robot, the control electronics and the software. The mechanical basis of the robot is formed by an RC car that has been modified with parts printed on a 3D printer. The control electronics is a Raspberry Pi version 4 single board computer, which is complemented by an Adeept Robot HAT. Most of the necessary peripherals are then connected to this extension. The operating software was written in Python and several use cases were created as part of the work. These examples include line recognition algorithms as well as a PSD controller.
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|
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Determination of Motion Parameters in Machine Vision
Dušek, Stanislav ; Horák, Karel (referee) ; Janáková, Ilona (advisor)
This thesis describe about determination of camera motion parameters in plane. At first there are introduce the basics of motion tracking, is focused to find out displacement between two input images. Below is describe the algorithm GoodFeatruresToTrack, which find out the most significant point in a first image. The point is search out the good point, which will be easy to track in next image, reduce the data volume and prepare the input information (array of significant point) for the algorithm Lucas-Kanade optical flow. In second part is deal with processing and utilization estimations optical flow. There is median filtration, below is describe computation of homogenous transformation, which describe all affine transformation in affine space. As the result are coordinates, which describe the shift between the two input images as X-axis and Y-axis value. The project used the library Open Computer Vision.
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