Title: DiffVL: Diffusion-Based Visual Localization on 2D Maps via BEV-Conditioned GPS Denoising Show Chinese title

Title: DiffVL：通过BEV条件GPS去噪的基于扩散的2D地图视觉定位

Abstract: Accurate visual localization is crucial for autonomous driving, yet existing methods face a fundamental dilemma: While high-definition (HD) maps provide high-precision localization references, their costly construction and maintenance hinder scalability, which drives research toward standard-definition (SD) maps like OpenStreetMap. Current SD-map-based approaches primarily focus on Bird's-Eye View (BEV) matching between images and maps, overlooking a ubiquitous signal-noisy GPS. Although GPS is readily available, it suffers from multipath errors in urban environments. We propose DiffVL, the first framework to reformulate visual localization as a GPS denoising task using diffusion models. Our key insight is that noisy GPS trajectory, when conditioned on visual BEV features and SD maps, implicitly encode the true pose distribution, which can be recovered through iterative diffusion refinement. DiffVL, unlike prior BEV-matching methods (e.g., OrienterNet) or transformer-based registration approaches, learns to reverse GPS noise perturbations by jointly modeling GPS, SD map, and visual signals, achieving sub-meter accuracy without relying on HD maps. Experiments on multiple datasets demonstrate that our method achieves state-of-the-art accuracy compared to BEV-matching baselines. Crucially, our work proves that diffusion models can enable scalable localization by treating noisy GPS as a generative prior-making a paradigm shift from traditional matching-based methods. Show Chinese abstract

Abstract: 准确的视觉定位对于自动驾驶至关重要，但现有方法面临一个根本性困境：虽然高精度（HD）地图提供了高精度的定位参考，但其高昂的构建和维护成本阻碍了可扩展性，这推动了研究转向标准清晰度（SD）地图，如OpenStreetMap。 基于当前SD地图的方法主要集中在图像与地图之间的鸟瞰图（BEV）匹配，忽略了普遍存在且噪声大的GPS信号。 尽管GPS易于获取，但在城市环境中会受到多路径误差的影响。 我们提出了DiffVL，这是第一个使用扩散模型将视觉定位重新表述为GPS去噪任务的框架。 我们的关键见解是，当以视觉BEV特征和SD地图为条件时，噪声GPS轨迹隐式地编码了真实姿态分布，可以通过迭代扩散细化来恢复。 与之前的BEV匹配方法（例如OrienterNet）或基于Transformer的配准方法不同，DiffVL通过联合建模GPS、SD地图和视觉信号来学习逆转GPS噪声扰动，在不依赖HD地图的情况下实现了分米级的精度。 在多个数据集上的实验表明，我们的方法相比BEV匹配基线达到了最先进的准确性。 至关重要的是，我们的工作证明了扩散模型可以通过将噪声GPS视为生成先验来实现可扩展的定位，从而实现了从传统匹配方法到新范式的转变。