VLM引导的视觉地点识别用于行星级地理定位
VLM-Guided Visual Place Recognition for Planet-Scale Geo-Localization
AI总结：
提出VLM与检索式VPR结合的混合框架，利用VLM生成先验约束搜索空间，再通过检索和重排序实现行星级地理定位，在街道和城市级别分别提升4.51%和13.52%。
摘要：
从单张图像进行行星级地理定位（本质上是绑架机器人问题的高级或极端版本）是导航、自动驾驶和灾害响应等应用中的一项基础且具有挑战性的任务，原因在于位置、环境条件和场景变化的巨大多样性。传统的基于检索的地理定位方法在可扩展性和感知混叠方面存在困难，而基于分类的方法缺乏泛化能力且需要大量训练数据。视觉-语言模型（VLM）的最新进展通过利用上下文理解和推理提供了一种有前景的替代方案。然而，尽管VLM实现了高精度，但它们往往容易产生幻觉且缺乏可解释性，使其作为独立解决方案不可靠。在这项工作中，我们提出了一种新颖的混合地理定位框架，结合了VLM与基于检索的视觉地点识别（VPR）方法的优势。我们的方法首先利用VLM生成先验，有效引导和约束检索搜索空间。然后我们执行检索步骤，接着通过重排序机制根据特征相似性和与初始估计坐标的接近度选择最合理的地理匹配。我们在多个地理定位基准上评估了我们的方法，结果表明它始终优于先前的最先进方法，特别是在街道级别（提升高达4.51%）和城市级别（提升高达13.52%）。我们的结果证明，VLM生成的地理先验与VPR相结合能够产生可扩展、鲁棒且准确的地理定位系统。
英文摘要：
Geo-localization from a single image at planet scale (essentially an advanced or extreme version of the kidnapped robot problem) is a fundamental and challenging task in applications such as navigation, autonomous driving and disaster response due to the vast diversity of locations, environmental conditions, and scene variations. Traditional retrieval-based methods for geo-localization struggle with scalability and perceptual aliasing, while classification-based approaches lack generalization and require extensive training data. Recent advances in vision-language models (VLMs) offer a promising alternative by leveraging contextual understanding and reasoning. However, while VLMs achieve high accuracy, they are often prone to hallucinations and lack interpretability, making them unreliable as standalone solutions. In this work, we propose a novel hybrid geo-localization framework that combines the strengths of VLMs with retrieval-based visual place recognition (VPR) methods. Our approach first leverages a VLM to generate a prior, effectively guiding and constraining the retrieval search space. We then employ a retrieval step, followed by a re-ranking mechanism that selects the most geographically plausible matches based on feature similarity and proximity to the initially estimated coordinates. We evaluate our approach on multiple geo-localization benchmarks and show that it consistently outperforms prior state-of-the-art methods, particularly at street (up to 4.51%) and city level (up to 13.52%). Our results demonstrate that VLM-generated geographic priors in combination with VPR lead to scalable, robust, and accurate geo-localization systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
