发表机构
Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙国家研究委员会-加泰罗尼亚理工大学机器人与工业自动化研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Spotter是GPS退化环境下的实时视觉定位框架，以建筑立面为地理参考，在巴塞罗那数据集上精度优于里程计基线、与先进地图方法相当且帧率更高。
AI 中文摘要
在密集城市环境中，机器人和可穿戴平台的精准视觉定位仍然是一项挑战。现有方法通常依赖GPS进行绝对定位，但由于多径传播，GPS信号在城市峡谷中经常出现退化。因此，视觉里程计等标准解决方案会随时间出现无法缓解的漂移，而地图匹配技术不仅难以获取所需的可靠GPS先验，还因计算量过大无法在边缘设备上实时运行。为解决这些局限，我们提出Spotter，这是一个鲁棒且实时的视觉定位框架，以建筑立面作为可靠的全局地理参考来源，同时在GPS信号可用时保留其集成能力。在离线阶段，Spotter通过语义分割立面、将多视图立体深度与制图数据配对，处理Google街景全景图以构建紧凑的度量数据库；运行时，查询图像通过级联检索与几何验证流水线匹配，以恢复细粒度的全局相机定位。我们在新采集的数据集上对Spotter进行基准测试，该数据集由可穿戴智能眼镜在巴塞罗那多个城区采集的行人序列组成。实验结果显示，Spotter的性能优于基于里程计的基线方法，达到与最先进的基于地图的方法相当的定位精度，同时帧率显著更高。
英文摘要
Accurate visual localization on robotic and wearable platforms remains challenging in dense urban environments. Existing methodologies typically rely on GPS for absolute positioning, yet GPS signals frequently degrade in urban canyons due to multipath propagation. Consequently, standard solutions like visual odometry suffer from unmitigated drift over time, while map-matching techniques struggle to acquire the reliable GPS priors they need, on top of being too computationally heavy for real-time edge execution. To address these limitations, we propose Spotter, a robuts and real-time visual localization framework that uses building facades as a reliable source of global geo-reference, while retaining the capability to integrate GPS signals when available. In an offline stage, Spotter processes Google Street View panoramas by semantically segmenting facades and pairing multi-view stereo depth with cartographic data to build a compact metric database. At runtime, query images are matched via a cascaded retrieval and geometric verification pipeline to recover fine-grained global camera localization. We benchmark Spotter on a newly collected dataset of pedestrian sequences acquired with wearable smart glasses across several districts of Barcelona. Experimental results show that Spotter outperforms odometry-based baselines and achieves localization accuracy comparable to state-of-the-art map-based methods while operating at significantly higher frame rates.
