PROCEDIMENTO PARA AUTOLOCALIZAÇÃO DE ROBÔS EM CASAS DE VEGETAÇÃO UTILIZANDO DESCRITORES SURF: Implementação Sequencial e Paralela

• Ano de defesa: 2015
• Autor(a) principal: Orloski, Andrey
• Orientador(a): Rocha, Jose Carlos Ferreira da
• Banca de defesa: Falate, Rosane , Fey, Emerson
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: UNIVERSIDADE ESTADUAL DE PONTA GROSSA
• Programa de Pós-Graduação: Programa de Pós Graduação Computação Aplicada
• Departamento: Computação para Tecnologias em Agricultura
• País: BR
• Palavras-chave em Português: autolocalização; Agrobot; algoritmo SURF; casa de vegetação; visão computacional
• Palavras-chave em Inglês: self-localization; Agrobot; SURF algorithm; greenhouse; computer vision
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://tede2.uepg.br/jspui/handle/prefix/130

This paper describes a procedure for self-localization of mobile and autonomous agrobots in greenhouses, that is, the determination of the robot's position relative to a coordinate system,using procedures and computational resources. The proposed procedure uses computer vision techniques to recognize markers objects in the greenhouse and, from them, estimate the coordinate of the robot in a parallel plane to the surface of the stove. The detection of the presence of markers in the scene is performed using the SURF algorithm. To enable the estimation of coordinates, based on data contained in a single image, the method of Rahman et al. (2008), which consists in etermining the distance between a camera and a marker object has been extended to allow the coordinate calculation. The performance of the procedure was evaluated in three experiments. In the first experiment, the objective was to verify, in the laboratory, the influence of image resolution on accuracy. The results indicate that by reducing the image resolution, the range of the process is impaired for the recognition of the markers. These results also show that by reducing the resolution, the error in estimating the coordinates relative to the distance between the camera and the marker increases. The second experiment ran a test that evaluates the computational performance of the SURF algorithm, in terms of computing time, in the image processing. This is important because agrobots usually need to perform tasks that require the processing power in real time. The results of this test indicate that the efficiency of the procedure drops with the increase of image resolution. A second test compared the processing time of two implementations of the algorithm. One explores a sequential version of the SURF algorithm and another uses a parallel implementation. The results of this test suggest that the parallel implementation is more efficient in all tested resolutions, with an almost constant proportionate improvement.The third experiment was performed in a greenhouse to evaluate the performance of the proposed procedure in the environment for which it was designed. Field results were similar to the laboratory, but indicate that lighting variations require parameter settings of the SURF algorithm.