Tactile Elastography

Elasticity is one of the representative parameters that reflect the mechanical properties of soft materials. Detecting the underneath elasticity distribution called elastography is a key step for understanding and interacting with objects. Existing solutions for capturing the interior elasticity distribution typically rely on expensive apparatus. In this work, the dense tactile signal captured by the high-resolution vision-based tactile sensor is introduced as a new modality for reconstructing 3-D elasticity distribution. We propose a model-based method, which exploits the tactile maps from active pressing trials for the elastography task. The interior elasticity distribution for nonrigid objects is reconstructed from an inverse physics model. We analyze the credibility of the estimated elasticity distribution obtained from our method. Varying design factors are also discussed. We experiment our method on a set of synthesized 3-D models and physical models in robot-assisted scenes. Various experimental results have been gathered, demonstrating the efficacy of our approach in perceiving elasticity distribution.