OmniLoc: A Geometry-Aware Foundation Model for Anchor-Free UE Localization Across Diverse Indoor Environments

2026-06-09

AI Summary

This work addresses the challenge of anchor-free indoor localization across diverse environments, where variations in building geometry, detectable access point sets, and signal heterogeneity hinder performance. To this end, the authors propose OmniLoc—the first foundation model for anchor-free localization that operates directly on raw wireless measurements. OmniLoc achieves end-to-end cross-environment localization through unified input tokenization, a geometry-aware Transformer for feature extraction, and geometry-embedded conditional position regression. Evaluated on both a large-scale in-house dataset and public benchmarks, OmniLoc significantly outperforms existing methods. Ablation studies confirm that each core component contributes meaningfully to overall performance, demonstrating the model’s strong generalization capability across heterogeneous indoor settings.

Abstract

Indoor localization from wireless measurements remains challenging in large-scale deployments due to substantial variation in building geometry, the set of detectable access points (APs), and the heterogeneity of received signals. Existing learning-based methods often perform well only in limited settings and degrade under environmental shifts, making robust anchor-free localization across diverse indoor environments notoriously difficult. In this paper, we present OmniLoc, an environment-interactive foundation model for anchor-free user equipment localization across diverse indoor environments. To the best of our knowledge, OmniLoc is the first foundation-model-based approach built directly on wireless measurements for this task. OmniLoc is built on three key designs. First, a unified input tokenization module converts heterogeneous wireless measurements into a common representation that is more amenable to learning. Second, a geometry-aware Transformer performs AP-aware feature extraction by emphasizing dominant APs while aggregating complementary evidence from supporting APs. Third, a geometry-aware location estimation module conditions regression on geometric embeddings to produce geometrically consistent location predictions. We evaluate OmniLoc on both a large-scale in-house dataset and a public benchmark dataset. Results show that OmniLoc significantly outperforms existing methods, consistently improves existing backbones when its design components are integrated, and demonstrates strong generalization in cross-environment evaluations.

Problem

• indoor localization
• anchor-free
• wireless measurements
• environmental generalization
• heterogeneous signals

Innovation

• foundation model
• anchor-free localization
• geometry-aware Transformer
• wireless measurements
• indoor positioning