A Monocular Vision System for Playing Soccer in Low Color Information Environments

TL;DR

This paper introduces a monocular vision system for humanoid soccer robots that effectively detects field features and objects in low color information environments, ensuring robustness and real-time performance.

Contribution

The paper presents a novel vision system that relies on brightness and texture, enabling soccer robots to perceive their environment despite reduced color cues, adaptable to recent rule changes.

Findings

System is robust to lighting variations

Successfully used in RoboCup 2015

Operates in real-time on humanoid robots

Abstract

Humanoid soccer robots perceive their environment exclusively through cameras. This paper presents a monocular vision system that was originally developed for use in the RoboCup Humanoid League, but is expected to be transferable to other soccer leagues. Recent changes in the Humanoid League rules resulted in a soccer environment with less color coding than in previous years, which makes perception of the game situation more challenging. The proposed vision system addresses these challenges by using brightness and texture for the detection of the required field features and objects. Our system is robust to changes in lighting conditions, and is designed for real-time use on a humanoid soccer robot. This paper describes the main components of the detection algorithms in use, and presents experimental results from the soccer field, using ROS and the igus Humanoid Open Platform as a testbed. The proposed vision system was used successfully at RoboCup 2015.