BioGraphLoc: Bio-Inspired Static Structure Graph for Robust LiDAR Global Localization

Robust and accurate 6-DoF pose measurement in previously mapped environments is a cornerstone of autonomous systems, enabling tasks such as navigation, loop closure, and safety-critical decision-making. In large-scale urban or unstructured settings, Light Detection and Ranging (LiDAR)-based pose measurement has become increasingly vital due to its geometric fidelity and resilience to illumination or appearance changes. This paper presents BioGraphLoc, a novel bio-inspired measurement framework that determines global poses by extracting and encoding structural nodes from a Moving Object Segmentation (MOS)-filtered static structure graph, leveraging biologically inspired mechanisms such as place cells and head-direction cells. Specifically, we first construct a Spatial Static Topology Graph (StatiGraph) from mid-scale static clusters, and encode each node with a lightweight 4D FroZone descriptor that captures depth-level grouping, local geometry, and topology. For reliable correspondence detection, we introduce a Neural Activation Consistency (NAC) strategy and incorporate Hebbian-inspired memory updates to track node stability across time. The final 6-DoF pose is then measured via robust Truncated Least Squares (TLS) optimization with angular regularization. Experiments on the KITTI, HeLiPR, and our custom dataset demonstrate that the proposed method achieves state-of-the-art measurement accuracy, particularly excelling in structurally ambiguous and dynamically cluttered environments where traditional approaches often degrade, with the highest localization success rate reaching 91.32%.