轻量级视觉定位框架，实现高效大场景图像目标导航
LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation
- 分层定位架构，从粗到细逐步估计相机位姿
- 使用学习特征匹配与几何求解器，降低存储开销
- 新数据集支持无地图重定位，适合实际部署
本文提出LiteVLoc，一种基于轻量级拓扑-度量地图的分层视觉定位框架。系统包含三个依次执行的模块，以粗到细的方式估计相机位姿。不同于依赖详细3D表示的主流方法，LiteVLoc通过基于学习的特征匹配和几何求解器实现度量位姿估计，显著降低存储开销。同时引入一个针对无地图重定位任务的新数据集。大量实验在仿真与真实场景中验证了系统的性能，证明其在大规模部署下的精度与效率。代码与数据将公开。
原文摘要 · Abstract (English)
This paper presents LiteVLoc, a hierarchical visual localization framework that uses a lightweight topo-metric map to represent the environment. The method consists of three sequential modules that estimate camera poses in a coarse-to-fine manner. Unlike mainstream approaches relying on detailed 3D representations, LiteVLoc reduces storage overhead by leveraging learning-based feature matching and geometric solvers for metric pose estimation. A novel dataset for the map-free relocalization task is also introduced. Extensive experiments including localization and navigation in both simulated and real-world scenarios have validate the system's performance and demonstrated its precision and efficiency for large-scale deployment. Code and data will be made publicly available.
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