Geometry-Aware Volumetric Data Stitching Using Local Surface Mapping and Robot Optical Coherence Tomography

Abstract

Optical coherence tomography (OCT) has been widely used for high-fidelity biological tissue scanning but is traditionally limited to small lateral fields of view that preclude large-area scanning. To overcome this problem, we propose an integration of an OCT sensor to a 6-DOF robot arm end-effector combined with a geometry-aware stitching model for surface and volumetric data stitching. We firstly develop a simple but efficient Robot-OCT calibration method by using a three-marker calibration pattern and implement an optimization solver. Given a pre-defined trajectory, a local planner is developed to update the sensor pose by using the OCT point cloud information in order to maintain the effective imaging depth based on the distance and orientation constraints. The system calibration method is verified through repeated experiments with the three-marker targets and the result shows an average testing error of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.132 \pm 0.071 ~\text{mm}$</tex>. The geometry-aware OCT stitching framework is demonstrated based on the experiments of different scanning trajectories and 3D-printed phantoms for large-area scanning. The OCT stitched point cloud is compared with the ground truth from the phantom CAD model and the result show an average surface alignment error of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$0.441 \pm 0.241 ~\text{mm}$</tex> for the path following tasks.