Map Slammer. Densifying Scattered KSLAM 3D Maps with Estimated Depth
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http://hdl.handle.net/10045/94751
Títol: | Map Slammer. Densifying Scattered KSLAM 3D Maps with Estimated Depth |
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Autors: | Torres Cámara, José Miguel |
Director de la investigació: | Cazorla, Miguel | Gomez-Donoso, Francisco |
Centre, Departament o Servei: | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial |
Paraules clau: | SLAM | 3D maps | Point clouds | Depth perception | Depth estimation |
Àrees de coneixement: | Ciencia de la Computación e Inteligencia Artificial |
Data de publicació: | 27-de juliol-2019 |
Data de lectura: | 22-de juliol-2019 |
Resum: | There are a range of small-size robots that cannot afford to mount a three-dimensional sensor due to energy, size or power limitations. However, the best localisation and mapping algorithms and object recognition methods rely on a three-dimensional representation of the environment to provide enhanced capabilities. Thus, in this work, a method to create a dense three-dimensional representation of the environment by fusing the output of a keyframe-based SLAM (KSLAM) algorithm with predicted point clouds is proposed. It will be demonstrated with quantitative and qualitative results the advantages of this method, focusing in three different measures: localisation accuracy, densification capabilities and accuracy of the resultant three-dimensional map. |
URI: | http://hdl.handle.net/10045/94751 |
Idioma: | eng |
Tipus: | info:eu-repo/semantics/bachelorThesis |
Drets: | Licencia Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 |
Apareix a la col·lecció: | Grado en Ingeniería Robótica - Trabajos Fin de Grado |
Arxius per aquest ítem:
Arxiu | Descripció | Tamany | Format | |
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SLAM_usando_tecnicas_de_deep_learning_Torres_Camara_Jose_Miguel.pdf | 7,18 MB | Adobe PDF | Obrir Vista prèvia | |
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