OWP-IMU: An RSS-based Optical Wireless and IMU Indoor Positioning Dataset

TL;DR

This paper introduces a comprehensive open-source dataset combining RSS, IMU, and ground truth data for indoor localization, enabling research and benchmarking of optical wireless positioning systems in various scenarios.

Contribution

It provides a new, extensive dataset with multi-scenario data and benchmark tests, facilitating advancements in RSS-based optical wireless indoor positioning.

Findings

RSS and IMU data improve localization accuracy

Benchmark tests demonstrate enhanced performance with sensor fusion

Dataset covers diverse indoor environments and conditions

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 RSS-based 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 two indoor environments. One environment has no obstacles, and the other has a metal column as an obstacle to represent a non-line-of-sight (NLOS) scenario. We recorded data with a vehicle at three different speeds (low, medium and high). The dataset includes over 110 k data points and covers more than 80 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. The dataset OWP-IMU is open source1 to support further research on indoor localization methods.