  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.
