Map-based LiDAR pose tracking is essential for long-term autonomous operation, where onboard map priors need be compact for scalable storage and fast retrieval, while online observations are often partial, repetitive, and heavily occluded. We propose Graph-Loc, a graph-based localization framework that tracks the platform pose against compact structural map priors represented as a lightweight point-line graph. Such priors can be constructed from heterogeneous sources commonly available in practice, including polygon outlines vectorized from occupancy/grid maps and CAD/model/floor-plan layouts. For each incoming LiDAR scan, Graph-Loc extracts sparse point and line primitives to form an observation graph, retrieves a pose-conditioned visible subgraph via LiDAR ray simulation, and performs scan-to-map association through unbalanced optimal transport with a local graph-context regularizer. The unbalanced formulation relaxes mass conservation, improving robustness to missing, spurious, and fragmented structures under occlusion. To enhance stability in low-observability segments, we estimate information anisotropy from the refinement normal matrix and defer updates along weakly constrained directions until sufficient constraints reappear. Experiments on public benchmarks, controlled stress tests, and real-world deployments demonstrate accurate and stable tracking with KB-level priors from heterogeneous map sources, including under geometrically degenerate and sustained occlusion and in the presence of gradual scene changes.

翻译：基于地图的激光雷达位姿跟踪对于长期自主运行至关重要，其中机载地图先验需保持紧凑以实现可扩展的存储与快速检索，而在线观测往往存在部分性、重复性且严重遮挡。本文提出Graph-Loc，一种基于图的定位框架，该框架针对以轻量级点-线图表示的紧凑结构地图先验进行平台位姿跟踪。此类先验可从实践中常见的异构数据源构建，包括从占据/栅格地图矢量化得到的多边形轮廓以及CAD/模型/平面布局图。对于每一帧输入的激光雷达扫描，Graph-Loc提取稀疏点与线基元以构建观测图，通过激光雷达射线模拟检索位姿约束下的可见子图，并利用带有局部图上下文正则化项的非平衡最优传输方法实现扫描到地图的关联。非平衡形式松弛了质量守恒约束，从而提升了在遮挡情况下对缺失、冗余及碎片化结构的鲁棒性。为增强低可观测性区段的稳定性，我们从优化法矩阵中估计信息各向异性，并沿弱约束方向延迟更新直至重新出现足够约束。在公开基准测试、受控压力实验及实际部署中的验证表明，该方法能够利用来自异构地图源的千字节级先验实现准确且稳定的跟踪，包括在几何退化、持续遮挡以及场景逐渐变化的情况下。