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Visual Navigation of the Vehicle
Jaššo, Kamil ; Richter, Miloslav (referee) ; Horák, Karel (advisor)
The thesis consists of retrieval of known solutions of autonomous vehicle navigation. Thesis further describes options for controling autonomous vehicle, and using of sensors in autonomous vehicles. From different types of sensors are selected two types, that are most suitable for visual navigation of the vehicle. Thesis describes the function and way of using these two types of sensors in the visual navigation of the vehicle. The program for obtaining and saving data from selected sensors is also part of the thesis.
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Object Detection in the Laser Scans Using Convolutional Neural Networks
Zelenák, Michal ; Kodym, Oldřich (referee) ; Veľas, Martin (advisor)
This work is focused on road segmentation in laser scans, using a convolutional neural network. To achieve this goal, which will find application in the field of road maintenance, convolutional neural networks have been used for their flexibility and speed. The work brings implementation and modifications of the existing method, which solves the problem by using a fully connected convolutional neural network. Used modifications include, for example using of various parameters for the loss function, the use of a different number of classes in the network model and dataset. The effect of the modification was experimentally verified and the accuracy of 96.12%, and the value for F-measure 95.02% were achieved.
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Object Detection in the Laser Scans Using Convolutional Neural Networks
Zelenák, Michal ; Kodym, Oldřich (referee) ; Veľas, Martin (advisor)
This work is focused on road segmentation in laser scans, using a convolutional neural network. To achieve this goal, which will find application in the field of road maintenance, convolutional neural networks have been used for their flexibility and speed. The work brings implementation and modifications of the existing method, which solves the problem by using a fully connected convolutional neural network. Used modifications include, for example using of various parameters for the loss function, the use of a different number of classes in the network model and dataset. The effect of the modification was experimentally verified and the accuracy of 96.12%, and the value for F-measure 95.02% were achieved.
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Visual Navigation of the Vehicle
Jaššo, Kamil ; Richter, Miloslav (referee) ; Horák, Karel (advisor)
The thesis consists of retrieval of known solutions of autonomous vehicle navigation. Thesis further describes options for controling autonomous vehicle, and using of sensors in autonomous vehicles. From different types of sensors are selected two types, that are most suitable for visual navigation of the vehicle. Thesis describes the function and way of using these two types of sensors in the visual navigation of the vehicle. The program for obtaining and saving data from selected sensors is also part of the thesis.
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