An Enhanced FingerVision for Contact Spatial Surface Sensing

Vision-based tactile sensor is a promising solution for tactile sensing. In this paper, we customized FingerVision sensor with multi-layer structure and biomimetic features, and used it to verify a two-step spatial surface sensing method. For the custom sensor, the multi-layer structure imitated the skin and tissue, and the sensing area and frequency range were consistent with the Meissner's corpuscles and Merkel discs. The two-step sensing method was verified through spatial reconstruction and surface recognition. First, we employed the integration method for surface reconstruction based on the gradients estimated from the marker displacements. The similarity between the reconstructed and the actual contact surface was 85.33% in average according to the evaluation on heights. Then we improved the features by correcting the marker displacements according to the boundary condition during surface reconstruction for surface recognition implementing K-Nearest Neighbor (KNN) algorithm. The accuracy was up to 99.26% when the training set was only 15% for 19 surface classes. While with the features extracted from the original coordinates, the accuracy was only 95.58%. The two-step sensing method implied that the surface reconstruction and recognition were related, and the reconstruction step indeed helped to the recognition. Additionally, the excellent recognition capability indicated the practical value of our custom sensor and the feature processing method in surface recognition through touch.

Automated summary

In this study, a vision-based tactile sensor was customized and used to verify a two-step spatial surface sensing method. The results showed that surface reconstruction and recognition were related, and that the reconstruction step helped improve recognition accuracy. Additionally, the custom sensor demonstrated excellent recognition capability, indicating practical value in surface recognition through touch.