GelFinger: A Novel Visual-Tactile Sensor With Multi-Angle Tactile Image Stitching

Visual-tactile sensors that use a camera to capture the deformation of a soft gel layer have become popular in recent years. However, these sensors have a limited receptive field, which can hinder their ability to perceive tactile information effectively. In this letter, we propose a novel visual-tactile sensor named GelFinger that closely resembles the human finger and is well-suited for detecting various complex surfaces. The GelFinger sensor is equipped with an embedded miniature motor that allows for the adaptation of the camera pose and the scanning of a large contact area. During the detection process, the camera rotates to multiple angles to capture the tactile image of the contact area. To stitch together the tactile images obtained at different camera poses, we use an As-Projective-As-Possible image stitching algorithm to form a global view of the contact. We demonstrate the effectiveness of the GelFinger sensor in assessing large surfaces by using it to reconstruct curved crack outlines. Comparative experimental results show that the proposed sensor can effectively detect cracks and has the potential to assist humans in detecting defects on curved surfaces of infrastructure such as pipelines.