{
  "annotation_id": "ab571aa6-628d-4c76-9476-f6195dcef602",
  "date_created": "2026-02-17T05:53:16.137000Z",
  "date_modified": "2026-02-17T05:53:16.137000Z",
  "file_hash": "5f7fc7740624fc9d7352742ad67b606ce8bf90c98ec1fccc72f96225c67c10bd",
  "private": false,
  "record": {
    "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\u0027s floor to achieve self-localization on the basketball field.",
    "arxiv_id": "2601.08713",
    "authors": [
      "Naren Medarametla",
      "Sreejon Mondal"
    ],
    "categories": [
      "cs.RO",
      "cs.AI",
      "cs.CV"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "title": "Real-Time Localization Framework for Autonomous Basketball Robots",
    "url": "https://arxiv.org/abs/2601.08713",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "abb1a84b-5da9-4575-947b-e9e8b72426bf",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}