RobotCub implementation of real-time least-square fitting of ellipses

This paper presents the implementation of a new algorithm for pattern recognition in machine vision developed in our laboratory applied to the RobotCub humanoid robotics platform simulator. The algorithm is a robust and direct method for the least-square fitting of ellipses to scattered data. RobotCub is an open source platform, born to study the development of neuro-scientific and cognitive skills in human beings, especially in children. By the estimation of the surrounding objects properties (such as dimensions, distances, etc...) a subject can create a topographic map of the environment, in order to navigate through it without colliding with obstacles. In this work we implemented the method of the least-square fitting of ellipses of Maini (EDFE), previously developed in our laboratory, in a robotics context. Moreover, we compared its performance with the hough transform, and others least-square ellipse fittings techniques. We used our system to detect spherical objects, and we applied it to the simulated RobotCub platform. We performed several tests to prove the robustness of the algorithm within the overall system, and finally we present our results.