A Qualitative-Probabilistic Approach to Autonomous Mobile Robot Self Localisation and Self Vision Calibration

Autor(es)

Pereira, Valquiria Fenelon ; Cozman, Fabio Gagliardi ; Santos, Paulo Eduardo ; Martins, Murilo Fernandes ; Pozo, ATR ; Camargo, HD ; Furtado, V ; Pinheiro, V

• Tipo de documento: Artigo Científico
• Fonte: 2013 BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS); v. N/A, p. 6-pg., 2013-01-01.

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. (AU)

• Processo FAPESP: 12/12640-1 - Aprendizado por Demonstrações em Cenários Cooperativos de Interação Humano-robô
• Beneficiário: Murilo Fernandes Martins
• Modalidade de apoio: Bolsas no Brasil - Pós-Doutorado
• Processo FAPESP: 12/04089-3 - Raciocínio espacial colaborativo para múltiplos robôs
• Beneficiário: Paulo Eduardo Santos
• Modalidade de apoio: Auxílio à Pesquisa - Regular