Knowledge-based autonomous dynamic colour calibration

Abstract

Colour labeling is critical to the real-time performance of colour-based vision systems and is used for low-level vision by most RoboCup 2002 physically based teams. Unfortunately, colour labeling is sensitive to changes in illumination and manual calibration is both time consuming and error prone. In this paper, we present KADC, a robust method for Knowledge-based Autonomous Dynamic Colour Calibration. By utilising the known geometry of the environment, landmarks are identified independent of colour classifications. Colour information from these landmarks is used to construct colour clusters of arbitrary shape. Clusters are dynamically updated through actions and by the use of a similarity metric, the Earth Mover's Distance (EMD). We apply KADC to the RoboCup Legged League, generating a colourtable purely from geometrical knowledge of the environment and dynamically update this colortable to compensate for non-uniform changes in lighting conditions.