Machine learning has been considered a promising approach for indoor localization. Nevertheless, the sample efficiency, scalability, and generalization ability remain open issues of implementing learning-based algorithms in practical systems. In this paper, we establish a zero-shot learning framework that does not need real-world measurements in a new communication environment. Specifically, a graph neural network that is scalable to the number of access points (APs) and mobile devices (MDs) is used for obtaining coarse locations of MDs. Based on the coarse locations, the floor-plan image between an MD and an AP is exploited to improve localization accuracy in a floor-plan-aided deep neural network. To further improve the generalization ability, we develop a synthetic data generator that provides synthetic data samples in different scenarios, where real-world samples are not available. We implement the framework in a prototype that estimates the locations of MDs. Experimental results show that our zero-shot learning method can reduce localization errors by around 30% to 55% compared with three baselines from the existing literature.

中文翻译：

---

平面图辅助室内定位：零样本学习框架、数据集和原型


机器学习被认为是室内定位的一种有前途的方法。然而，在实际系统中实现基于学习的算法时，样本效率、可扩展性和泛化能力仍然是一个悬而未决的问题。在本文中，我们建立了一个零样本学习框架，不需要在新的通信环境中进行现实世界的测量。具体来说，可扩展至接入点（AP）和移动设备（MD）数量的图神经网络用于获得MD的粗略位置。基于粗略位置，利用 MD 和 AP 之间的平面图图像来提高平面图辅助深度神经网络中的定位精度。为了进一步提高泛化能力，我们开发了一种合成数据生成器，可以在无法获得真实样本的情况下提供不同场景下的合成数据样本。我们在估计 MD 位置的原型中实现了该框架。实验结果表明，与现有文献中的三个基线相比，我们的零样本学习方法可以将定位误差减少约 30% 至 55%。