Graph-Loc: Robust Graph-Based LiDAR Pose Tracking with Compact Structural Map Priors under Low Observability and Occlusion

2026-02-09

AI Summary

This work addresses the challenge of LiDAR pose tracking under conditions of low observability, severe occlusion, and structural repetition by proposing a robust method leveraging a compact point-line graph prior. A lightweight structured map encoding point and line primitives is constructed offline, while sparse geometric primitives extracted from incoming LiDAR scans form an observation graph. Visible subgraphs are retrieved via ray-casting simulation, and correspondence is established through unbalanced optimal transport augmented with a local graph context regularizer. The method innovatively incorporates an information anisotropy metric to dynamically suppress pose updates along poorly constrained directions. Requiring only kilobyte-scale heterogeneous map priors, the approach achieves accurate and stable tracking across public benchmarks, stress tests, and real-world deployments, effectively handling persistent occlusions, geometric degeneracy, and incremental scene changes.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies

Abstract

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.

Problem

• LiDAR pose tracking
• low observability
• occlusion
• compact map priors
• structural map

Innovation

• graph-based localization
• structural map priors
• unbalanced optimal transport
• LiDAR pose tracking
• information anisotropy