Towards Material Classification Of Scenes Using Active Thermography

By briefly heating the local environment with a heat lamp and observing what happens with a thermal camera, robots could potentially infer properties of their surroundings. However, this form of active thermography introduces large signal variations compared to traditional active thermography, which has typically been used to characterize small regions of materials in carefully controlled settings. We demonstrate that a data-driven approach with modern machine learning methods can be used to classify material samples over relatively large surface areas and variable distances. We also introduce the use of z-normalization to improve material classification and reduce variation due to distance and heating intensity. Our best performing algorithm achieved an overall accuracy of 77.7% for multi-class classification among 12 materials placed at varying distances (20 cm, 30 cm, and 40 cm). The observations were made for 5 seconds with 1s of heating and 4s of cooling. We also provide a demonstration of performance with a multi-material scene.