Real-Time Localization Framework for Autonomous Basketball Robots

TL;DR

This paper presents a hybrid localization framework for autonomous basketball robots that combines classical and learning-based visual methods to enhance accuracy and reliability in dynamic competition environments.

Contribution

It introduces a novel hybrid localization algorithm that relies solely on visual data for self-localization in basketball robotics, improving upon existing methods.

Findings

Achieves high localization accuracy in dynamic environments

Enhances robot navigation and shooting precision

Demonstrates robustness with visual-only data

Abstract

Localization is a fundamental capability for autonomous robots, enabling them to operate effectively in dynamic environments. In Robocon 2025, accurate and reliable localization is crucial for improving shooting precision, avoiding collisions with other robots, and navigating the competition field efficiently. In this paper, we propose a hybrid localization algorithm that integrates classical techniques with learning based methods that rely solely on visual data from the court's floor to achieve self-localization on the basketball field.