TS-MapLoc: Large-Scale Indoor Object-Level Localization With Topological-Semantic Maps

Abstract

Large-scale indoor mapping and positioning with vision sensors is fundamental to a wide range of applications, such as robotic navigation and augmented reality. However, the rapidly increasing number of detectable objects and the expanded spatial coverage jointly introduce matching ambiguity and high computational cost. Fine-grained object maps can improve accuracy but often accumulate redundant observations and slow down localization, whereas overly compressed scene representations may discard essential semantic and structural cues and degrade robustness. To balance accuracy and efficiency for indoor spatial sensing, we propose TS-MapLoc, a map-centric object-level localization framework based on cross-layer semantic co-mapping. It builds a lightweight topological–semantic map that integrates multi-scale information from the image layer and the object layer, reducing redundancy while preserving key structural constraints. On top of this map, a cognition-inspired progressive localization strategy performs coarse-to-fine inference via stage-wise filtering under cross-layer semantic consistency, effectively narrowing the search space and stabilizing matching. The proposed method supports efficient and accurate object-level localization for built-environment applications.