OWP-IMU: An RSS-Based Optical Wireless and IMU Indoor Positioning Dataset
Fan Wu, Jorik De Bruycker, Daan Delabie, Nobby Stevens, François Rottenberg, Lieven De Strycker
Abstract
Received signal strength (RSS)-based optical wireless positioning (OWP) systems are becoming popular for indoor localization because they are low-cost and accurate. However, few open-source datasets are available to test and analyze RSSbased OWP systems. In this paper, we collected RSS values at a sampling frequency of 27 Hz, inertial measurement unit (IMU) at a sampling frequency of 200 Hz and the ground truth at a sampling frequency of 160 Hz in three indoor environments. The first scenario is obstacle-free, the second contains a metal column obstacle, and the third contains a paper rectangular obstacle, with both obstacles representing different non-line-of-sight (NLOS) scenarios. We recorded data with a vehicle at three different speeds (low, medium and high). The dataset includes over 160 k data points and covers more than 110 min. We also provide benchmark tests to show localization performance using only RSS-based OWP and improve accuracy by combining IMU data via extended kalman filter or transformer. The dataset OWPIMU and accompanying benchmark results are open source to support further research on indoor localization methods
BibTeX
@inproceedings{ral2025_owpimuanrssbased,
title = {OWP-IMU: An RSS-Based Optical Wireless and IMU Indoor Positioning Dataset},
author = {Fan Wu and Jorik De Bruycker and Daan Delabie and Nobby Stevens and François Rottenberg and Lieven De Strycker},
booktitle = {RA-L 2025},
year = {2025}
}