Robotic soccer is nowadays a popular research domain in the area of multi-robot systems. In the context of RoboCup, the Middle Size League is one of the most challenging. This paper presents an efficient omnidirectional vision system for real-time object detection, developed for the robotic soccer team of the University of Aveiro, CAMBADA. The vision system is used to find the ball and white lines, which are used for self-localization, as well as to find the presence of obstacles. Algorithms for detecting these objects and also for calibrating most of the parameters of the vision system are presented in this paper. We also propose an efficient approach for detecting arbitrary FIFA balls, which is an important topic of research in the Middle Size League.
The experimental results that we present show the effectiveness of our algorithms, both in terms of accuracy and processing time, as well as the results that the team has been achieving: 1st place in RoboCup 2008, 3rd place in 2009 and 1st place in the mandatory technical challenge in RoboCup 2009, where the robots have to play with an arbitrary standard FIFA ball.In this paper, we provide a comprehensive description of the vision system of the MSL CAMBADA team. Concerning the calibration of the intrinsic parameters of the digital camera, we propose an automated calibration algorithm that is used to configure the most important features of the camera, namely, the saturation, exposure, white-balance, gain and brightness. The proposed algorithm uses the histogram of intensities of the acquired images and a black and a white area, known in advance, to estimate the referred parameters. We also describe a general solution to calculate the robot centered distances map, exploring a back-propagation ray-tracing approach and the geometric properties of the mirror surface. To read this external content in full download the paper from the author archives at the University of Aveiro.http://robotica.ua.pt/CAMBADA/docs/Neves-2010a.pdf