{
  "id": "2410.18573",
  "version": "v1",
  "published": "2024-10-24T09:26:46.000Z",
  "updated": "2024-10-24T09:26:46.000Z",
  "title": "On Model-Free Re-ranking for Visual Place Recognition with Deep Learned Local Features",
  "authors": [
    "Tomáš Pivoňka",
    "Libor Přeučil"
  ],
  "comment": "12 pages, 9 figures",
  "categories": [
    "cs.CV",
    "cs.RO"
  ],
  "abstract": "Re-ranking is the second stage of a visual place recognition task, in which the system chooses the best-matching images from a pre-selected subset of candidates. Model-free approaches compute the image pair similarity based on a spatial comparison of corresponding local visual features, eliminating the need for computationally expensive estimation of a model describing transformation between images. The article focuses on model-free re-ranking based on standard local visual features and their applicability in long-term autonomy systems. It introduces three new model-free re-ranking methods that were designed primarily for deep-learned local visual features. These features evince high robustness to various appearance changes, which stands as a crucial property for use with long-term autonomy systems. All the introduced methods were employed in a new visual place recognition system together with the D2-net feature detector (Dusmanu, 2019) and experimentally tested with diverse, challenging public datasets. The obtained results are on par with current state-of-the-art methods, affirming that model-free approaches are a viable and worthwhile path for long-term visual place recognition.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2024-10-24T09:26:46.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "visual place recognition",
      "deep learned local features",
      "model-free re-ranking",
      "long-term autonomy systems",
      "standard local visual features"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 12,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}