Efficient Lines Detection for Robot Soccer
This addresses the need for efficient line detection in robot soccer, enabling real-time pose estimation on low-power platforms, but it is incremental as it builds on existing algorithms.
The paper tackled the problem of detecting soccer field lines for robot self-localization by presenting a lightweight method using the ELSED algorithm with RGB color classification and PSO for threshold calibration, achieving accuracy comparable to a state-of-the-art deep learning model with higher processing speed.
Self-localization is essential in robot soccer, where accurate detection of visual field features, such as lines and boundaries, is critical for reliable pose estimation. This paper presents a lightweight and efficient method for detecting soccer field lines using the ELSED algorithm, extended with a classification step that analyzes RGB color transitions to identify lines belonging to the field. We introduce a pipeline based on Particle Swarm Optimization (PSO) for threshold calibration to optimize detection performance, requiring only a small number of annotated samples. Our approach achieves accuracy comparable to a state-of-the-art deep learning model while offering higher processing speed, making it well-suited for real-time applications on low-power robotic platforms.