Title: ObjLoc: Indoor Camera Relocalization based on Open-Vocabulary Object-Level Mapping
Primary area: applications to robotics, autonomy, planning
Keywords: visual camera relocalization, object-level mapping, open vocabulary

Abstract: Indoor visual relocalization plays a key role in emerging spatial and embodied AI applications. However, prior research has predominantly focused on methods based on low-level vision. Despite notable progress, these methods inherently struggle to capture scene semantics and compositions, limiting their interpretability and interactivity. To address this limitation, we propose ObjLoc, a camera relocalization system designed to provide an intuition of scene object compositions and accurate pose estimation, which can be seamlessly reused in high-level tasks. Specifically, leveraging recent foundation models, we first introduce a multi-modal strategy to integrate open-vocabulary semantic knowledge for effective 2D-3D object matching. Additionally, we design an object-oriented reference frame and a corresponding retrieval strategy for pose priors, enabling extension to scalable scenes. To ensure robust and accurate pose optimization, we also propose a novel dual-path 2D Iterative Closest Pixel loss guided by object geometry. Experimental results demonstrate that ObjLoc achieves superior relocalization performance across various datasets. Our source code will be released upon acceptance.