Robot-Based Procedure for 3D Reconstruction of Abdominal Organs Using the Iterative Closest Point and Pose Graph Algorithms.

Image-based 3D reconstruction enables robot-assisted interventions and image-guided navigation, which are emerging technologies in laparoscopy. When a robotic arm guides a laparoscope for image acquisition, hand-eye calibration is required to know the transformation between the camera and the robot flange. The calibration procedure is complex and must be conducted after each intervention (when the laparoscope is dismounted for cleaning). In the field, the surgeons and their assistants cannot be expected to do so. Thus, our approach is a procedure for a robot-based multi-view 3D reconstruction without hand-eye calibration, but with pose optimization algorithms instead. In this work, a robotic arm and a stereo laparoscope build the experimental setup. The procedure includes the stereo matching algorithm Semi Global Matching from OpenCV for depth measurement and the multiscale color iterative closest point algorithm from Open3D (v0.19), along with the multiway registration algorithm using a pose graph from Open3D (v0.19) for pose optimization. The procedure is evaluated quantitatively and qualitatively on ex vivo organs. The results are a low root mean squared error (1.1-3.37 mm) and dense point clouds. The proposed procedure leads to a plausible 3D model, and there is no need for complex hand-eye calibration, as this step can be compensated for by pose optimization algorithms.