Zero-Shot Multi-View Indoor Localization via Graph Location Networks

TL;DR

This paper introduces Graph Location Networks (GLN), a neural network architecture for infrastructure-free, multi-view image-based indoor localization, including a zero-shot setting that predicts locations without prior data, outperforming existing methods.

Contribution

The paper presents a novel neural network architecture, GLN, and a zero-shot localization setting with Map2Vec, enabling accurate predictions at unseen locations without prior data collection.

Findings

GLN outperforms state-of-the-art methods in standard indoor localization.

The zero-shot extension achieves promising accuracy at unseen locations.

The approach eliminates the need for infrastructure and extensive data collection.

Abstract

Indoor localization is a fundamental problem in location-based applications. Current approaches to this problem typically rely on Radio Frequency technology, which requires not only supporting infrastructures but human efforts to measure and calibrate the signal. Moreover, data collection for all locations is indispensable in existing methods, which in turn hinders their large-scale deployment. In this paper, we propose a novel neural network based architecture Graph Location Networks (GLN) to perform infrastructure-free, multi-view image based indoor localization. GLN makes location predictions based on robust location representations extracted from images through message-passing networks. Furthermore, we introduce a novel zero-shot indoor localization setting and tackle it by extending the proposed GLN to a dedicated zero-shot version, which exploits a novel mechanism Map2Vec to train location-aware embeddings and make predictions on novel unseen locations. Our extensive experiments show that the proposed approach outperforms state-of-the-art methods in the standard setting, and achieves promising accuracy even in the zero-shot setting where data for half of the locations are not available. The source code and datasets are publicly available at https://github.com/coldmanck/zero-shot-indoor-localization-release.