In this work we describe the preparation of a time series dataset of inertial measurements for determining the surface type under a wheeled robot. The data consists of over 7600 labeled time series samples, with the corresponding surface type annotation. This data was used in two public competitions with over 1500 participant in total. Additionally, we describe the performance of state-of-art deep learning models for time series classification, as well as propose a baseline model based on an ensemble of machine learning methods. The baseline achieves an accuracy of over 68% with our nine-category dataset.
Cite
@article{arxiv.1905.00252,
title = {Surface Type Classification for Autonomous Robot Indoor Navigation},
author = {Francesco Lomio and Erjon Skenderi and Damoon Mohamadi and Jussi Collin and Reza Ghabcheloo and Heikki Huttunen},
journal= {arXiv preprint arXiv:1905.00252},
year = {2019}
}