Fast and Accurate Environment Modelling using Omnidirectional Vision

This paper describes an algorithm to detect obstacles and landmarks, using the omnidirectional vision system of a RoboCup robot, to build an internal representation of the robot’s environment. The restriction to pixels corresponding to an equally spaced grid on the floor around the robot and a biologically inspired fault-tolerant colour segmentation of this grid result in a fast and robust detection. The performance of the environment modelling concerning computation time and accuracy is addressed by comparing experimental results to object positions given by an absolute positioning system.