エネルギー特徴量を用いた深層学習による路面分類：3つの独立データセットにわたる比較分析
Leveraging Energy Features for Surface Classification with Deep Learning: A Comparative Analysis Across Three Independent Datasets
移動ロボットの路面分類において、エネルギー由来の特徴量を単独または慣性データと組み合わせて使用する手法を、3つの公開データセットで深層学習モデルを用いて評価し、単独で85-90%、併用で96-99%の精度を達成した。
著者: Alexander Belyaev, Oleg Kushnarev
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
The energy-based method remains a comparatively underexamined approach for surface classification in mobile robotics, despite promising results in constrained environments. This study evaluated the viability of using energy-derived features as either a standalone classification modality or as supplementary input to inertial data. A comprehensive evaluation was conducted across three publicly available datasets, comparing the performance of modern deep learning architectures including recurrent neural networks, convolutional neural networks, encoder-only transformers, and Mamba state-space models, under automated hyperparameter tuning and input sequence length optimization. The models achieved higher accuracy than previously reported values on all evaluated datasets, with the convolutional neural network yielding the highest overall performance. When relying exclusively on energy-based features, the models attained classification accuracies in the range of 85-90%, approximately 5-10% lower than those achieved when combined with inertial features (96-99%). Augmenting inertial data with energy features resulted in a consistent mean accuracy improvement of 1-2%. These findings indicate that classifiers relying solely on energy features offer sufficient accuracy for standalone deployment, while also providing a consistent gain when used in combination with other sensing modalities.