基于地板视觉信息的实时定位框架，提升篮球机器人自主导航能力
Real-Time Localization Framework for Autonomous Basketball Robots
- 融合经典方法与视觉学习算法实现无外源依赖定位
- 仅用赛场地板图像即可完成高精度实时定位
- 适用于比赛场景下机器人精准走位与避障
定位是自主机器人在动态环境中有效运行的基础能力。在 Robocon 2025 赛事中，精准可靠的定位对于提升投篮命中率、避免与其他机器人碰撞以及高效导航比赛场地至关重要。本文提出一种混合定位算法，结合经典技术与基于学习的方法，仅依赖赛场地板的视觉数据，实现篮球场上的自定位。该方法不依赖外部传感器或标记，通过分析地板纹理与图案变化，实时估计机器人位置与朝向，为自主篮球机器人提供稳定的空间感知支持。
原文摘要 · Abstract (English)
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.
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