A qualitative-probabilistic approach to autonomous mobile robot self localisation and self vision calibration
Nenhuma Miniatura disponível
Citações na Scopus
6
Tipo de produção
Artigo de evento
Data
2013-10-20
Autores
PEREIRA, V. F.
COZMAN, F. G.
Paulo Santos
MARTINS, M. F.
COZMAN, F. G.
Paulo Santos
MARTINS, M. F.
Orientador
Periódico
Proceedings - 2013 Brazilian Conference on Intelligent Systems, BRACIS 2013
Título da Revista
ISSN da Revista
Título de Volume
Citação
PEREIRA, V. F.; COZMAN, F. G.; SANTOS, P.; MARTINS, M. F. A qualitative-probabilistic approach to autonomous mobile robot self localisation and self vision calibration. Proceedings - 2013 Brazilian Conference on Intelligent Systems, BRACIS 2013, p157-162, Oct. 2013.
Texto completo (DOI)
Palavras-chave
Resumo
Typically, the spatial features of a robot's environment are specified using metric coordinates, and well-known mobile robot localisation techniques are used to track the exact robot position. In this paper, a qualitative-probabilistic approach is proposed to address the problem of mobile robot localisation. This approach combines a recently proposed logic theory called Perceptual Qualitative Reasoning about Shadows (PQRS) with a Bayesian filter. The approach herein proposed was systematically evaluated through experiments using a mobile robot in a real environment, where the sequential prediction and measurement steps of the Bayesian filter are used to both self-localisation and self-calibration of the robot's vision system from the observation of object's and their shadows. The results demonstrate that the qualitative-probabilistic approach effectively improves the accuracy of robot localisation, keeping the vision system well calibrated so that shadows can be properly detected. © 2013 IEEE.