Recent learning-based visual localization methods use global descriptors to disambiguate visually similar places, but existing approaches often derive these descriptors from geometric cues alone (e.g., covisibility graphs), limiting their discriminative power and reducing robustness in the presence of noisy geometric constraints. We propose an aggregator module that learns global descriptors consistent with both geometrical structure and visual similarity, ensuring that images are close in descriptor space only when they are visually similar and spatially connected. This corrects erroneous associations caused by unreliable overlap scores. Using a batch-mining strategy based solely on the overlap scores and a modified contrastive loss, our method trains without manual place labels and generalizes across diverse environments. Experiments on challenging benchmarks show substantial localization gains in large-scale environments while preserving computational and memory efficiency. Code is available at https://github.com/sontung/robust_scr.

翻译：近期基于学习的视觉定位方法采用全局描述符来区分视觉上相似的地点，但现有方法通常仅从几何线索（例如共视性图）推导这些描述符，限制了其判别能力，并在存在噪声几何约束时降低了鲁棒性。我们提出一种聚合模块，该模块学习与几何结构和视觉相似性均保持一致的全局描述符，确保图像仅在视觉相似且空间连通时在描述符空间中接近。这纠正了由不可靠重叠分数导致的错误关联。通过仅基于重叠分数的批次挖掘策略和修改后的对比损失，我们的方法无需人工地点标签即可训练，并能泛化至多样环境。在具有挑战性的基准测试上的实验表明，该方法在大规模环境中实现了显著的定位性能提升，同时保持了计算和内存效率。代码可在 https://github.com/sontung/robust_scr 获取。