Enhance Vision-based Tactile Sensors via Dynamic Illumination and Image Fusion
This work addresses the sensing quality issue for vision-based tactile sensors like DIGIT and GelSight, offering a retroactive software update for existing hardware and enabling new designs, though it is incremental as it builds on static illumination methods.
The paper tackled the problem of low-quality sensing in vision-based tactile sensors by using dynamic illumination patterns and image fusion, resulting in significant improvements in image contrast, sharpness, and background difference.
Vision-based tactile sensors use structured light to measure deformation in their elastomeric interface. Until now, vision-based tactile sensors such as DIGIT and GelSight have been using a single, static pattern of structured light tuned to the specific form factor of the sensor. In this work, we investigate the effectiveness of dynamic illumination patterns, in conjunction with image fusion techniques, to improve the quality of sensing of vision-based tactile sensors. Specifically, we propose to capture multiple measurements, each with a different illumination pattern, and then fuse them together to obtain a single, higher-quality measurement. Experimental results demonstrate that this type of dynamic illumination yields significant improvements in image contrast, sharpness, and background difference. This discovery opens the possibility of retroactively improving the sensing quality of existing vision-based tactile sensors with a simple software update, and for new hardware designs capable of fully exploiting dynamic illumination.