giaIndoorLoc – Auto-labeled WLAN + IMU dataset generated via VI-SLAM2tag

This repository holds the data that belongs to the publication:

M. Laska, T. Schulz, J. Grottke, C. Blut and J. Blankenbach, "VI-SLAM2tag: Low-Effort Labeled Dataset Collection for Fingerprinting-Based Indoor Localization",  [arXiv:2207.02668]

which is to appear at the 2022 IPIN conference. 

It is split into the following sub-parts:
  - giaIndoorLoc_raw: Raw data recorded via the VI-SLAM2tag android app (https://github.com/laskama/VI-SLAM2tag_app)
  - giaIndoorLoc: Annotated dataset (generated from giaIndoorLoc_raw)
  - evaluation_data: Raw trajectory data that is used during evaluation of labeling accuracy of VI-SLAM2tag (Control-Point + Total Station (Tachymeter))
  - model_evaluation: Model weights of fitted models used during baseline performance section (VII-B) of paper. Required for reproducing experiments with repo (https://github.com/laskama/mCELindoorLoc)

For a detailed description, please refer to the given paper and the additional github repositories that host the implementations:

- https://github.com/laskama/VI-SLAM2tag_post

- https://github.com/laskama/VI-SLAM2tag_app

- https://github.com/laskama/mCELindoorLoc