Real-Time Localization Framework for Autonomous Basketball Robots
This work addresses localization challenges for autonomous basketball robots in competitions like Robocon 2025, focusing on improving shooting precision and navigation, but it appears incremental as it combines existing techniques.
The paper tackles the problem of accurate and reliable localization for autonomous basketball robots in dynamic competition environments, proposing a hybrid algorithm that integrates classical and learning-based methods using visual data from the court floor to achieve self-localization.
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.