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

arXiv: https://arxiv.org/abs/2209.02270 · HTML: https://mlsanity.com/paper/2209.02270/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 31 references that are in the map.
- First posted on arXiv 6 Sep 2022.

## Builds on (most cited first, 1 of 1)

- [Efficient conversion from rotating matrix to rotation axis and angle by extending Rodrigues' formula](https://mlsanity.com/paper/1810.02999/) (2018)

## Abstract (arXiv)

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.