Laska Marius, Blankenbach Jörg
Geodetic Institute and Chair for Computing in Civil Engineering & Geo Information Systems, RWTH Aachen University, Mies-van-der-Rohe-Str. 1, 52074 Aachen, Germany.
Sensors (Basel). 2021 Mar 12;21(6):2000. doi: 10.3390/s21062000.
Location-based services (LBS) have gained increasing importance in our everyday lives and serve as the foundation for many smartphone applications. Whereas Global Navigation Satellite Systems (GNSS) enable reliable position estimation outdoors, there does not exist any comparable gold standard for indoor localization yet. Wireless local area network (WLAN) fingerprinting is still a promising and widely adopted approach to indoor localization, since it does not rely on preinstalled hardware but uses the existing WLAN infrastructure typically present in buildings. The accuracy of the method is, however, limited due to unstable fingerprints, etc. Deep learning has recently gained attention in the field of indoor localization and is also utilized to increase the performance of fingerprinting-based approaches. Current solutions can be grouped into models that either estimate the exact position of the user (regression) or classify the area (pre-segmented floor plan) or a reference location. We propose a model, DeepLocBox (DLB), that offers reliable area localization in multi-building/multi-floor environments without the prerequisite of a pre-segmented floor plan. Instead, the model predicts a bounding box that contains the user's position while minimizing the required prediction space (size of the box). We compare the performance of DLB with the standard approach of neural network-based position estimation and demonstrate that DLB achieves a gain in success probability by 9.48% on a self-collected dataset at RWTH Aachen University, Germany; by 5.48% for a dataset provided by Tampere University of Technology (TUT), Finland; and by 3.71% for the UJIIndoorLoc dataset collected at Jaume I University (UJI) campus, Spain.
基于位置的服务（LBS）在我们的日常生活中变得越来越重要，并且是许多智能手机应用程序的基础。虽然全球导航卫星系统（GNSS）能够在户外进行可靠的位置估计，但目前还不存在任何可比的室内定位黄金标准。无线局域网（WLAN）指纹识别仍然是一种很有前途且被广泛采用的室内定位方法，因为它不依赖于预安装的硬件，而是利用建筑物中通常存在的现有WLAN基础设施。然而，由于指纹不稳定等原因，该方法的准确性受到限制。深度学习最近在室内定位领域受到关注，并且也被用于提高基于指纹识别方法的性能。当前的解决方案可以分为两类模型，一类是估计用户的确切位置（回归），另一类是对区域（预分割的平面图）或参考位置进行分类。我们提出了一种模型，即深度定位框（DLB），它可以在多建筑物/多层环境中提供可靠的区域定位，而无需预分割的平面图。相反，该模型预测一个包含用户位置的边界框，同时最小化所需的预测空间（框的大小）。我们将DLB的性能与基于神经网络的位置估计的标准方法进行了比较，并证明DLB在德国亚琛工业大学自行收集的数据集上成功概率提高了9.48%；在芬兰坦佩雷理工大学（TUT）提供的数据集上提高了5.48%；在西班牙海梅一世大学（UJI）校园收集的UJIIndoorLoc数据集上提高了3.71%。
