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Road and path segmentation in images for autonomous driving scenario
Janíček, Ondřej ; Cihlář, Miloš (referee) ; Svědiroh, Stanislav (advisor)
This bachelor's thesis deals with the topic of segmentation of roads and paths for the purposes of autonomous driving. In the theoretical part, it deals with computer vision, simple segmentation methods, and practical solutions to the problem using convolutional neural networks and classical methods. In the practical part, the work deals with the collection of test data, the selection of a suitable programming language, and the selection of suitable libraries. Subsequently, the procedure for programming our own solution will be presented. Here it starts with pre-processing to convert the image into a grayscale image and filtering the noise, then finding the edges in the image using the Canny edge detector, followed by the definition of the region of interest, with the subsequent Hough transform to detect the straight lines in the image, and in the last stage, filtering the horizontal lines and averaging the remaining lines. At the end of the thesis, the results of the presented solution are compared with respect to robustness and computational complexity.
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Road Surface Detection
Melichar, Jiří ; Motlíček, Petr (referee) ; Hradiš, Michal (advisor)
This bachelor`s thesis deals with a method for road surface detection in picture and is inspired by work of S. Thrun and H. Dahlkamp. The method works with color models of road, which are adjusted to changing environment. Then these models are used to classify the picture. Output of this is the detected road. The method is thoroughly analyzed, implemented and tested. Test results are discussed and proposals for improvements are presented.
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Road Detection in the Camera Image
Šedo, Jan ; Luža, Radim (referee) ; Rozman, Jaroslav (advisor)
Bachelor's thesis deals with road detection in camera image. It contains overview of used methods in this area and their most frequented usage, brief introduction to image processing and to ROS framework. Then it gives proposal and details of implementation of application, which analyzes the image and performs segmentation in order to separate the road and it's surroundings by global thresholding method with use of ROS and OpenCV libraries. Application is tested on the data gathered for purposes of Robotour 2013 contest and on series of own recorded data. Finally it contains evaluation of results and discussion about future work on this project.
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Road Detection for Autonomous Car
Komora, Matúš ; Veľas, Martin (referee) ; Španěl, Michal (advisor)
This thesis deals with detection of the road adjacent to an autonomous vehicle. The road is recognition is based on the Velodyne LiDAR laser radar data. An existing solution is used and extended by machine learning - a Support Vector Machine with online learning. The thesis evaluates the existing solution and the new one using a KITTI dataset. The reliability of the road recognition is then computed using F-measure.
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Local Navigation of an Autonomous Mobile Robot
Herman, David ; Rozman, Jaroslav (referee) ; Orság, Filip (advisor)
This paper deals with the topic of design of a navigation system for an autonomous mobile robot in a park-like environment. Precisely, designing methods for road detection using available sensoric system, designing a mathematical model for fusion of these data, and suggesting a representation of an environment suitable for planning and local navigation.
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Road Detection for Autonomous Car
Komora, Matúš ; Veľas, Martin (referee) ; Španěl, Michal (advisor)
his thesis deals with detection of the road adjacent to an autonomous vehicle. The road is recognition is based on the Velodyne LiDAR laser radar data. An existing solution is used and extended by machine learning - a Support Vector Machine with online learning. The thesis evaluates the existing solution and the new one using a KITTI dataset. The reliability of the road recognition is then computed using F-measure.
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Road detection using data from mobile robot camera
Peška, Jaroslav ; Horák, Karel (referee) ; Petyovský, Petr (advisor)
Bachelor thesis deals with the problem of road detection by mobile robots using data obtained from its camera. First, current solutions are researched and considered for use in the proposed algorithm. Afterwards we define limit parameters of the entire solution. An automatic process using human-created reference was then devised to programatically determine the accuracy of individual versions of proposed solutions. First, an initial version of the solution was implemented, which was subsequently optimized and accelerated using GPGPU. Lastly, proposed algorithm is evaluated and possible future changes are outlined.
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Road Detection for Autonomous Car
Komora, Matúš ; Veľas, Martin (referee) ; Španěl, Michal (advisor)
This thesis deals with detection of the road adjacent to an autonomous vehicle. The road is recognition is based on the Velodyne LiDAR laser radar data. An existing solution is used and extended by machine learning - a Support Vector Machine with online learning. The thesis evaluates the existing solution and the new one using a KITTI dataset. The reliability of the road recognition is then computed using F-measure.
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Utilization of Convolution Neural Network Based Road Detection in Mobile Robot Localization
Krejsa, Jiří ; Věchet, Stanislav
Mobile robot on-road navigation requires fusion of both global and local sensory information with an emphasis on the road detection processing. The paper deals with the road detection based on convolution neural networks (CNN) using commonly available tools such as TensorFlow and Keras. The road is defined by its linear boundaries. Network output is formed by the road definition together with classification parameters and serves as a local sensor in Kalman filter based localization. CNN based road detection is currently capable to successfully detect about 90% of images.
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Road Detection Using Data From Mobile Robot
Camera
Peška, Jaroslav
The paper is focused on developing a road detection algorithm that uses only data from a mobile robot’s camera. Key requirements are low latency and relatively low power requirements. Presented algorithm makes use of machine learning, where the neural network is fed not only image data, but also select additional inputs.
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