Metodologia para construção de aplicações de rv e ra com marcadores naturais em cenários industriais

• Ano de defesa: 2017
• Autor(a) principal: GOMES JÚNIOR, Daniel Lima
• Orientador(a): PAIVA, Anselmo Cardoso de
• Banca de defesa: PAIVA, Anselmo Cardoso de , SILVA, Aristófanes Corrêa , PAUCA, Vicente Leonardo , DUAILIBE, Allan Kardec , RAPOSO, Alberto Barbosa
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal do Maranhão
• Programa de Pós-Graduação: PROGRAMA DE PÓS-GRADUAÇÃO EM CIÊNCIA DA COMPUTAÇÃO/CCET
• Departamento: DEPARTAMENTO DE INFORMÁTICA/CCET
• País: Brasil
• Palavras-chave em Português: Marcador Natural; Realidade Aumentada; Realidade Virtual; Haar-like Features; Sistema Elétrico de Potência; Virtual Reality;
• Palavras-chave em Inglês: Natural Marker; Augmented Reality; Haar-like features; Electric Power Systems
• Área do conhecimento CNPq: Sistemas Elétricos de Potência
• Link de acesso: https://tedebc.ufma.br/jspui/handle/tede/2011

This research proposes a methodology for development of Virttual Reality (VR) and Augmented Reality (AR) aplications, using natural markers for industrial scenarios. The proposed methodology uses the object annotation concept and visualization proposals are presented both for development of VR as for AR environments. In VR environments, the methodology is applied for object detection step of the semi-automatic environment development. On the other hand, in AR environments, is presented the concept of georreferenced natural markers, which use the georreferenced data integrated with object detection process using image processing techniques. The energy substations scenarios were used as case study for both approaches. Architectures are presented for construction and data visualization in industrial environments. Both for VR as for AR approaches, this work proposes using 3D natural markers based in Haar-like features for object training and detection process. The results enable the equipment detection at different points of view, within the operating scenario. Besides that, in AR, it enables the pose estimation in real-time using ORB features, while in VR it enables the semi-automatic object detection, which are used as information points for inclusion of virtual information. Several industrial scenarios, and especially the energy sector, has a high degree of complexity in the information processing and visualization. In this sense, beyond the 3D natural markers methodology, this work presents new visualization applications for industrial scenario visualization in VR and AR approaches.