Scalable and maintainable map representations are fundamental to enabling large-scale visual navigation and facilitating the deployment of robots in real-world environments. While collaborative localization across multi-session mapping enhances efficiency, traditional structure-based methods struggle with high maintenance costs and fail in feature-less environments or under significant viewpoint changes typical of crowd-sourced data. To address this, we propose OPENNAVMAP, a lightweight, structure-free topometric system leveraging 3D geometric foundation models for on-demand reconstruction. Our method unifies dynamic programming-based sequence matching, geometric verification, and confidence-calibrated optimization to robust, coarse-to-fine submap alignment without requiring pre-built 3D models. Evaluations on the Map-Free benchmark demonstrate superior accuracy over structure-from-motion and regression baselines, achieving an average translation error of 0.62m. Furthermore, the system maintains global consistency across 15km of multi-session data with an absolute trajectory error below 3m for map merging. Finally, we validate practical utility through 12 successful autonomous image-goal navigation tasks on simulated and physical robots. Code and datasets will be publicly available in https://rpl-cs-ucl.github.io/OpenNavMap_page.

翻译：可扩展且可维护的地图表示是实现大规模视觉导航和促进机器人在现实环境中部署的基础。尽管多会话建图中的协同定位能提升效率，但传统的基于结构的方法面临高昂的维护成本，且在特征缺失环境或众包数据中常见的显著视角变化下容易失效。为解决这些问题，我们提出了OPENNAVMAP——一种轻量级的无结构拓扑系统，该系统利用三维几何基础模型实现按需重建。我们的方法将基于动态规划的序列匹配、几何验证以及置信度校准优化统一起来，实现了从粗到精的鲁棒子图对齐，且无需预建三维模型。在Map-Free基准测试上的评估表明，本方法在精度上超越了运动恢复结构和回归基线方法，平均平移误差达到0.62米。此外，该系统在15公里多会话数据上保持了全局一致性，地图合并的绝对轨迹误差低于3米。最后，我们通过在仿真和实体机器人上成功完成的12项自主图像目标导航任务验证了其实用性。代码与数据集将在 https://rpl-cs-ucl.github.io/OpenNavMap_page 公开提供。