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http://hdl.handle.net/10609/118646
Title: | Detecció visual de bucles en fons marins mitjançant xarxes neuronals |
Author: | Gálvez Santos, Jordi |
Director: | Ventura, Carles |
Tutor: | Burguera Burguera, Antonio |
Abstract: | An autonomous vehicle placed in a not known environment must be able to be located and moved without supervision. SLAM wants to resolve this problem. Identify when a robot has previously passed through a given point is called loop closure and is an important step on SLAM methodology. There are various mechanisms for resolve loop closure problem. Compare images with machine learning is one of these techniques. The purpose of this work is to create a Neural Network to detect loop closures on seabed. The loop closure detection is performed by images of seabed taken by robots. The input images in Neural Network will be transformed to create a synthetic image, generating a pair of images (the original and the altered). A global descriptor (HOG) will be calculated on one of these loop images and the Network will be trained with the other image to find a similar descriptor. With this training, the Neural Network will learn to generate similar descriptors from pairs of images involved in loop closures. On the resolution of this problem we will use Keras on Python. We built a Neural Network that has been evaluated and then refined to provide better results. |
Keywords: | computer vision deep learning SLAM |
Document type: | info:eu-repo/semantics/masterThesis |
Issue Date: | 2-Jun-2020 |
Publication license: | http://creativecommons.org/licenses/by-nc-sa/3.0/es/ |
Appears in Collections: | Bachelor thesis, research projects, etc. |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
jordigsPresentacio.mp4 | Presentació del TFM | 145,4 MB | MP4 | View/Open |
jordigsCode.zip | Implementació principal de la xarxa neuronal | 21,05 kB | Unknown | View/Open |
jordigsTFM0620memòria.pdf | Memòria del TFM | 17,05 MB | Adobe PDF | View/Open |
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