For high-level geo-spatial applications and intelligent robotics, accurate global pose information is of crucial importance. Map-aided localization is a universal approach to overcome the limitations of global navigation satellite system (GNSS) in challenging environments. However, current solutions face challenges in terms of mapping flexibility, storage burden and re-localization performance. In this work, we present SF-Loc, a lightweight visual mapping and map-aided localization system, whose core idea is the map representation based on sparse frames with dense but compact depth, termed as visual structure frames. In the mapping phase, multi-sensor dense bundle adjustment (MS-DBA) is applied to construct geo-referenced visual structure frames. The local co-visbility is checked to keep the map sparsity and achieve incremental mapping. In the localization phase, coarse-to-fine vision-based localization is performed, in which multi-frame information and the map distribution are fully integrated. To be specific, the concept of spatially smoothed similarity (SSS) is proposed to overcome the place ambiguity, and pairwise frame matching is applied for efficient and robust pose estimation. Experimental results on the cross-season dataset verify the effectiveness of the system. In complex urban road scenarios, the map size is down to 3 MB per kilometer and stable decimeter-level re-localization can be achieved. The code will be made open-source soon (https://github.com/GREAT-WHU/SF-Loc).

翻译：对于高层次地理空间应用与智能机器人技术而言，精确的全局位姿信息至关重要。地图辅助定位是克服全球导航卫星系统（GNSS）在挑战性环境中局限性的通用方法。然而，现有解决方案在建图灵活性、存储负担与重定位性能方面仍面临挑战。本文提出SF-Loc，一种轻量级视觉建图与地图辅助定位系统，其核心思想是基于具有密集而紧凑深度的稀疏帧（称为视觉结构帧）的地图表示方法。在建图阶段，采用多传感器密集光束法平差（MS-DBA）构建地理参考的视觉结构帧，并通过局部共视性检查保持地图稀疏性，实现增量式建图。在定位阶段，执行由粗到精的视觉定位，充分融合多帧信息与地图分布特性。具体而言，提出空间平滑相似度（SSS）概念以克服地点模糊性问题，并采用成对帧匹配实现高效鲁棒的位姿估计。跨季节数据集上的实验结果验证了系统的有效性。在复杂城市道路场景中，地图存储量可降至每公里3 MB，并能实现稳定的分米级重定位精度。代码即将开源（https://github.com/GREAT-WHU/SF-Loc）。