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

TL;DR

This paper presents a high-precision indoor localization dataset created using an AR-based system that accurately annotates wireless signal data with less than 0.05 meters error, enabling better evaluation of indoor positioning algorithms.

Contribution

The authors introduce a novel AR-based positioning technique and a high-resolution dataset for precise indoor localization data annotation.

Findings

AR system reduces positional error to under 0.05 meters

High-quality dataset enables accurate evaluation of wireless localization algorithms

Method improves annotation precision for indoor positioning research

Abstract

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.