Vision Based Object Recognition and Localisation by a Wireless Connected Distributed Robotic Systems
Object recognition and localisation are important processes in computer vision and robotics. Advances in computer vision have resulted in many object recognition techniques, but most of them are computationally very intensive and require robots with powerful processing systems. For small robots, the...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2012 |
| País: | España |
| Institución: | Universitat Autònoma de Barcelona |
| Repositorio: | Dipòsit Digital de Documents de la UAB |
| Idioma: | inglés |
| OAI Identifier: | oai:ddd.uab.cat:102552 |
| Acceso en línea: | https://ddd.uab.cat/record/102552 https://dx.doi.org/urn:doi:10.5565/rev/elcvia.486 |
| Access Level: | acceso abierto |
| Palabra clave: | Object Recognition Localisation Multi-camera tracking |
| Sumario: | Object recognition and localisation are important processes in computer vision and robotics. Advances in computer vision have resulted in many object recognition techniques, but most of them are computationally very intensive and require robots with powerful processing systems. For small robots, these techniques are not applicable because of the constraints of execution time. In this study, an optimised implementation of SURF based recognition technique is presented. Suitable image pre-processing techniques were developed which reduced the recognition time on small robots with limited processing resources. The recognition time was reduced from 39 seconds to 780 milliseconds. This recognition technique was adopted by a team of small robots which were given prior training to search for objects of interest in the environment. For the localisation of the robots and objects a new template, designed for passive markers based tracking, was introduced. These markers were placed on the top of each robot and they were tracked by the two ceiling mounted cameras. The information from both sources, that is ceiling mounted cameras and team of robots, was used collectively to localise the objects in the environment. The objects were localised with an error ranging from 2.8cm to 5.2cm from their actual positions in the test arena which has the dimensions of 150x163cm. |
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