Real-Time Localization Framework for Autonomous Basketball Robots
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Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 17 references that are in the map. First posted on arXiv 13 Jan 2026.
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References · 7
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
- 2017Axiomatic Attribution for Deep Networkscited by 3,384
- 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,495
- 2022Robot Operating System 2: Design, Architecture, and Uses In The Wildcited by 199
- 2023Understanding URDF: A Dataset and Analysiscited by 7
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