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

Author(s)

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

• Document type: Journal article
• Source: 2013 BRAZILIAN CONFERENCE ON INTELLIGENT SYSTEMS (BRACIS); v. N/A, p. 6-pg., 2013-01-01.

Abstract

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)

• FAPESP's process: 12/12640-1 - Learning by Demonstration in Cooperative Human-robot Interaction Scenarios
• Grantee: Murilo Fernandes Martins
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 12/04089-3 - Collaborative spatial reasoning for a multi-robot system
• Grantee: Paulo Eduardo Santos
• Support Opportunities: Regular Research Grants