Leveraging Energy Features for Surface Classification with Deep Learning: A Comparative Analysis Across Three Independent Datasets
arXiv:2606.18698v1 Announce Type: cross Abstract: 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, enc
The continuous advancements in deep learning architectures and increased accessibility of computational resources enable more sophisticated analysis of previously 'underexamined' data modalities like energy-derived features for robotic perception.
Improving surface classification accurately and robustly is critical for advanced mobile robotics, impacting everything from autonomous navigation to industrial automation and defence applications.
The demonstrated viability of energy-derived features, either standalone or complementary to inertial data, expands the toolkit for robotic perception and could lead to more robust and versatile mobile robots.
- · Robotics manufacturers
- · Deep learning researchers
- · Automation industries
- · Companies reliant on single-modality robotic perception
- · Traditional sensor manufacturers
- · Less robust robotic platforms
Energy-based surface classification becomes a standard module in mobile robotic perception systems.
More reliable autonomous navigation and manipulation in unstructured and unknown environments, expanding robotic deployment.
Enhanced robot capabilities lead to accelerated adoption of AI-driven automation in dangerous or remote tasks, shifting labor dynamics and increasing operational efficiency across multiple sectors.
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Read at arXiv cs.AI