Sensor and Information Fusion Applied to a Robotic Soccer Team

This paper is focused on the sensor and information fusion techniques used by a robotic soccer team. Due to the fact that the sensor information is affected by noise, and taking into account the multi-agent environment, these techniques can significantly improve the accuracy of the robot world model. One of the most important elements of the world model is the robot self-localisation. Here, the team localisation algorithm is presented focusing on the integration of visual and compass information. To improve the ball position and velocity reliability, two different techniques have been developed. A study of the visual sensor noise is presented and, according to this analysis, the resulting noise variation depending on the distance is used to define a Kalman filter for ball position. Moreover, linear regression is used for velocity estimation purposes, both for the ball and the robot. This implementation of linear regression has an adaptive buffer size so that, on hard deviations from...

• Type: Conference
• Year: 2009
• Where: ROBOCUP
• Authors: João Silva, Nuno Lau, João Rodrigues, José Luís Azevedo, António J. R. Neves
• Keywords: Ball Position, Kalman filter, Linear Regression, ROBOCUP 2009, Robotics