A triangle histogram for object classification by tactile sensing
| Autor: | Monroe Kennedy, Kostas Daniilidis, M. Ani Hsieh, Mabel M. Zhang |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
0209 industrial biotechnology
business.industry 010401 analytical chemistry Robotic hand Point cloud Pattern recognition 02 engineering and technology 01 natural sciences 0104 chemical sciences 020901 industrial engineering & automation Sampling (signal processing) Robustness (computer science) Histogram Robot Computer vision Artificial intelligence Invariant (mathematics) business Mathematics |
| Zdroj: | IROS |
| DOI: | 10.1109/iros.2016.7759724 |
| Popis: | We present a new descriptor for tactile 3D object classification. It is invariant to object movement and simple to construct, using only the relative geometry of points on the object surface. We demonstrate successful classification of 185 objects in 10 categories, at sparse to dense surface sampling rate in point cloud simulation, with an accuracy of 77.5% at the sparsest and 90.1% at the densest. In a physics-based simulation, we show that contact clouds resembling the object shape can be obtained by a series of gripper closures using a robotic hand equipped with sparse tactile arrays. Despite sparser sampling of the object's surface, classification still performs well, at 74.7%. On a real robot, we show the ability of the descriptor to discriminate among different object instances, using data collected by a tactile hand. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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