Machine Learning for RealisticBall Detection in RoboCup SPL

TL;DR

This paper presents a machine learning-based ball detection module for RoboCup SPL, integrated into the official code, tested in various environments, and used in competitions, aiming to aid the RoboCup community.

Contribution

It introduces a machine learning approach for realistic ball detection, integrated into the B-Human code, with open-source code and deployment in RoboCup competitions.

Findings

Effective in indoor and outdoor environments

Successfully used in RoboCup German Open 2017

Provides a ready-to-use software module

Abstract

In this technical report, we describe the use of a machine learning approach for detecting the realistic black and white ball currently in use in the RoboCup Standard Platform League. Our aim is to provide a ready-to-use software module that can be useful for the RoboCup SPL community. To this end, the approach is integrated within the official B-Human code release 2016. The complete code for the approach presented in this work can be downloaded from the SPQR Team homepage at http://spqr.diag.uniroma1.it and from the SPQR Team GitHub repository at https://github.com/SPQRTeam/SPQRBallPerceptor. The approach has been tested in multiple environments, both indoor and outdoor. Furthermore, the ball detector described in this technical report has been used by the SPQR Robot Soccer Team during the competitions of the Robocup German Open 2017. To facilitate the use of our code by other teams, we have prepared a step-by-step installation guide.