Low cost GelSight with UV Markings: Feature Extraction of Objects Using AlexNet and Optical Flow without 3D Image Reconstruction
GelSight sensor has been used to study microgeometry of objects since 2009 in tactile sensing applications. Elastomer, reflective coating, lighting, and camera were the main challenges of making a GelSight sensor within a short period. The recent addition of permanent markers to the GelSight was a new era in shear/slip studies. In our previous studies, we introduced Ultraviolet (UV) ink and UV LEDs as a new form of marker and lighting respectively. UV ink markers are invisible using ordinary LED but can be made visible using UV LED. Currently, recognition of objects or surface textures using GelSight sensor is done using fusion of camera only images and GelSight captured images with permanent markings. Those images are fed to Convolutional Neural Networks (CNN) to classify objects. However, our novel approach in using low cost GelSight sensor with UV markings, the 3D height map to 2D image conversion, and the additional non Gelsight captured images for training the CNN can be eliminated. AlexNet and optical flow algorithm have been used for feature recognition of five coins without UV markings and shear/slip of the coin in GelSight with UV markings respectively. Our results on confusion matrix show that, on average coin recognition can reach 93.4% without UV markings using AlexNet. Therefore, our novel method of using GelSight with UV markings would be useful to recognize full/partial object, shear/slip, and force applied to the objects without any 3D image reconstruction.
2020 IEEE International Conference on Robotics and Automation (ICRA)