Surface Type Classification for Autonomous Robot Indoor Navigation
This work addresses surface type classification for wheeled robots in indoor environments, but it is incremental as it focuses on dataset creation and baseline benchmarking.
The authors tackled the problem of classifying surface types for autonomous robot indoor navigation by creating a dataset of over 7600 labeled inertial measurement time series samples and used it in public competitions. They reported that a baseline ensemble model achieved over 68% accuracy on a nine-category dataset.
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.