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Robot Localization Using Camera
Heřman, Petr ; Španěl, Michal (referee) ; Beran, Vítězslav (advisor)
The objective of this work is to design a simple localization method and its implementation in robot operating system ROS. This method uses a monocular camera as the only sensor and estimates the position in a known map. In experiments with prototypes are tested key points of type SURF, SIFT and ORB.
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Mobile Robot Localization Using Camera
Vaverka, Filip ; Orság, Filip (referee) ; Rozman, Jaroslav (advisor)
This thesis describes design and implementation of an approach to the mobile robot localization. The proposed method is based purely on images taken by a monocular camera. The described solution handles localization as an association problem and, therefore, falls in the category of topological localization methods. The method is based on a generative probabilistic model of the environment appearance. The proposed solution is capable to eliminate some of the difficulties which are common in traditional localization approaches.
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Detection of object position
Baáš, Filip ; Janáková, Ilona (referee) ; Richter, Miloslav (advisor)
Master’s thesis deals with object pose estimation using monocular camera. As an object is considered every rigid, shape fixed entity with strong edges, ideally textureless. Object position in this work is represented by transformation matrix, which describes object translation and rotation towards world coordinate system. First chapter is dedicated to explanation of theory of geometric transformations and intrinsic and extrinsic parameters of camera. This chapter also describes detection algorithm Chamfer Matching, which is used in this work. Second chapter describes all development tools used in this work. Third, fourth and fifth chapter are dedicated to practical realization of this works goal and achieved results. Last chapter describes created application, that realizes known object pose estimation in scene.
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Deep Neural Networks for Classifying Objects in an Image
Mlynarič, Tomáš ; Zemčík, Pavel (referee) ; Hradiš, Michal (advisor)
This paper deals with classifying objects using deep neural networks. Whole scene segmentation was used as main algorithm for the classification purpose which works with video sequences and obtains information between two video frames. Optical flow was used for getting information from the video frames, based on which features maps of a~neural network are warped. Two neural network architectures were adjusted to work with videos and experimented with. Results of the experiments show, that using videos for image segmentation improves accuracy (IoU) compared to the same architecture working with images.
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Detection of object position
Baáš, Filip ; Janáková, Ilona (referee) ; Richter, Miloslav (advisor)
Master’s thesis deals with object pose estimation using monocular camera. As an object is considered every rigid, shape fixed entity with strong edges, ideally textureless. Object position in this work is represented by transformation matrix, which describes object translation and rotation towards world coordinate system. First chapter is dedicated to explanation of theory of geometric transformations and intrinsic and extrinsic parameters of camera. This chapter also describes detection algorithm Chamfer Matching, which is used in this work. Second chapter describes all development tools used in this work. Third, fourth and fifth chapter are dedicated to practical realization of this works goal and achieved results. Last chapter describes created application, that realizes known object pose estimation in scene.
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Deep Neural Networks for Classifying Objects in an Image
Mlynarič, Tomáš ; Zemčík, Pavel (referee) ; Hradiš, Michal (advisor)
This paper deals with classifying objects using deep neural networks. Whole scene segmentation was used as main algorithm for the classification purpose which works with video sequences and obtains information between two video frames. Optical flow was used for getting information from the video frames, based on which features maps of a~neural network are warped. Two neural network architectures were adjusted to work with videos and experimented with. Results of the experiments show, that using videos for image segmentation improves accuracy (IoU) compared to the same architecture working with images.
|
|
|
Robot Localization Using Camera
Heřman, Petr ; Španěl, Michal (referee) ; Beran, Vítězslav (advisor)
The objective of this work is to design a simple localization method and its implementation in robot operating system ROS. This method uses a monocular camera as the only sensor and estimates the position in a known map. In experiments with prototypes are tested key points of type SURF, SIFT and ORB.
|
|
|
Mobile Robot Localization Using Camera
Vaverka, Filip ; Orság, Filip (referee) ; Rozman, Jaroslav (advisor)
This thesis describes design and implementation of an approach to the mobile robot localization. The proposed method is based purely on images taken by a monocular camera. The described solution handles localization as an association problem and, therefore, falls in the category of topological localization methods. The method is based on a generative probabilistic model of the environment appearance. The proposed solution is capable to eliminate some of the difficulties which are common in traditional localization approaches.
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