{
  "id": 13027,
  "url": "https://arxiv.org/abs/2607.17699",
  "title": "SLAM in Low-Light Environments: Project Report",
  "summary": "Simultaneous localization and mapping (SLAM) is one of the fundamental problems in robotics, as it enables autonomous operations in real-world scenarios. Under low illumination, reduced contrast, sensor noise, and motion blur degrade both feature extraction and feature matching, while compensating with LiDAR, depth, or thermal sensors raises cost, power draw, and integration complexity. Existing benchmarks remain dominated by well-lit indoor or daylight sequences, leaving open how far SLAM with",
  "authors": "Oleh Basystyi, Anna Stasyshyn, Oleksandr Kosovan, Yaroslav Prytula",
  "category": "research",
  "topics": "agents-autonomy,environment",
  "orgs": null,
  "regions": null,
  "published_at": "2026-07-19T20:00:00.000Z",
  "fetched_at": "2026-07-25T05:10:48.796Z",
  "source_slug": "hf-daily",
  "source_name": "HuggingFace Daily Papers",
  "source_homepage": "https://huggingface.co/papers",
  "ethics_ai_record_url": "https://ethics.ai/record/13027",
  "original_url": "https://arxiv.org/abs/2607.17699",
  "evidence_status": "source-only",
  "attribution": "via ethics.ai"
}