论文标题

具有高精度位置注释的室内本地化数据集和数据收集框架

An Indoor Localization Dataset and Data Collection Framework with High Precision Position Annotation

论文作者

Daniş, F. Serhan, Naskali, A. Teoman, Cemgil, A. Taylan, Ersoy, Cem

论文摘要

我们引入了一种新型技术和相关的高分辨率数据集,该数据集旨在精确评估基于无线信号的室内定位算法。该技术实现了基于增强现实(AR)的定位系统,该系统用于注释具有高精度位置数据的无线信号参数数据样本。我们在装饰有AR标记的区域中跟踪实用且低成本的可导航相机设置和蓝牙低能(BLE)信标的位置。我们通过使用冗余标记来最大程度地提高基于AR的本地化的性能。摄像机捕获的视频流经过一系列标记识别,子集选择和过滤操作,以产生高度精确的姿势估计。我们的结果表明,我们可以将AR定位系统的位置误差降低到0.05米以下的速率。然后,将位置数据用于注释BLE数据,这些数据由驻留在环境中的传感器同时捕获,因此,构建具有接地真实的无线信号数据集,该数据集允许准确评估基于无线信号的本地化系统。

We introduce a novel technique and an associated high resolution dataset that aims to precisely evaluate wireless signal based indoor positioning algorithms. The technique implements an augmented reality (AR) based positioning system that is used to annotate the wireless signal parameter data samples with high precision position data. We track the position of a practical and low cost navigable setup of cameras and a Bluetooth Low Energy (BLE) beacon in an area decorated with AR markers. We maximize the performance of the AR-based localization by using a redundant number of markers. Video streams captured by the cameras are subjected to a series of marker recognition, subset selection and filtering operations to yield highly precise pose estimations. Our results show that we can reduce the positional error of the AR localization system to a rate under 0.05 meters. The position data are then used to annotate the BLE data that are captured simultaneously by the sensors stationed in the environment, hence, constructing a wireless signal data set with the ground truth, which allows a wireless signal based localization system to be evaluated accurately.

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