{
  "abstract": "Reliable visual place recognition (VPR) under dynamic real-world conditions is critical for autonomous robots, yet conventional deep networks remain limited by high computational and energy demands. Inspired by the mammalian navigation system, we introduce SpikeVPR, a bio-inspired neuromorphic framework that leverages event-based cameras and spiking neural networks (SNNs) to learn compact, invariant place descriptors from a limited number of exemplars, enabling robust VPR across severe changes in illumination, viewpoint, and appearance. SpikeVPR is trained end-to-end using surrogate gradient learning and incorporates EventDilation, a novel augmentation strategy enhancing robustness to speed and temporal variations. Across three challenging benchmarks (Brisbane-Event-VPR, NSAVP and NYC-Event-VPR), SpikeVPR matches the performance of state-of-the-art deep networks while reducing model size by 50 × and lowering estimated energy consumption by 30–250 × , based on an analytical energy model, enabling efficient real-time deployment on mobile and neuromorphic hardware. These results demonstrate that spike-based coding offers an efficient pathway toward robust VPR in complex, changing environments.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "name": "Geoffroy Keime"
    },
    {
      "name": "Nicolas Cuperlier"
    },
    {
      "name": "Benoit R Cottereau"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/8c20ws7wrgkzk.bib",
  "cited_by": 0,
  "doi": "10.1088/2634-4386/ae8a77",
  "fulltext_url": null,
  "id": "8c20ws7wrgkzk",
  "issns": [
    "2634-4386"
  ],
  "landing_url": "https://hal.science/hal-05847260v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.info"
    ],
    [
      "hal",
      "0.sdv"
    ],
    [
      "openalex",
      "2208|Advanced Memory and Neural Computing"
    ]
  ],
  "openalex": "W7168270368",
  "pdf_url": "https://hal.science/hal-05847260/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-28",
  "publisher": "IOPScience",
  "ris": "https://research.draconitic.com/paper/8c20ws7wrgkzk.ris",
  "sources": [
    {
      "first_seen": "2026-11-02T12:20:44Z",
      "native_id": "hal-05847260",
      "source": "hal",
      "url": "https://hal.science/hal-05847260v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:0.sdv",
      "subfield": "general"
    }
  ],
  "title": "Event-driven neuromorphic vision enables energy-efficient visual place recognition",
  "title_text": "Event-driven neuromorphic vision enables energy-efficient visual place recognition",
  "type": "peer-reviewed",
  "updated_at": "2026-11-03T12:20:17Z",
  "url": "https://research.draconitic.com/paper/8c20ws7wrgkzk",
  "venue": "Neuromorphic Computing and Engineering",
  "versions": []
}