OCT as both a shape sensor and a tomographic imager for large-scale freeform robotic scanning.

To overcome the limitations of optical coherence tomography (OCT) in imaging large-scale freeform objects, we propose a methodological framework that utilizes OCT as both a shape sensor and a tomographic imager in robotic scanning. Our approach integrates a deep-learning-based surface detection algorithm to counter OCT artifacts and an adaptive robotic arm pose adjustment algorithm for sensing and imaging uneven objects. We demonstrate the effectiveness and superiority of our method on various objects, achieving high-resolution, large-scale tomographic imaging that adeptly manages OCT artifacts and surface irregularities. We think this work may contribute to expanding the applicability of OCT in both medical and industrial scenarios.