Energy features support 85-90% surface classification accuracy with DL models across three datasets and yield 1-2% gains when fused with inertial data.
Surface Type Classification for Autonomous Robot Indoor Navigation
1 Pith paper cite this work. Polarity classification is still indexing.
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abstract
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.
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cs.RO 1years
2026 1verdicts
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Leveraging Energy Features for Surface Classification with Deep Learning: A Comparative Analysis Across Three Independent Datasets
Energy features support 85-90% surface classification accuracy with DL models across three datasets and yield 1-2% gains when fused with inertial data.