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Laparoscopic Field of View (FOV) control is one of the most fundamental andimportant components in Minimally Invasive Surgery (MIS), nevertheless, thetraditional manual holding paradigm may easily bring fatigue to surgicalassistants, and misunderstanding between surgeons also hinders assistants toprovide a high-quality FOV. Targeting this problem, we here present adata-driven framework to realize an automated laparoscopic optimal FOV control.To achieve this goal, we offline learn a motion strategy of laparoscoperelative to the surgeon's hand-held surgical tool from our in-house surgicalvideos, developing our control domain knowledge and an optimal view generator.To adjust the laparoscope online, we first adopt a learning-based method tosegment the two-dimensional (2D) position of the surgical tool, and furtherleverage this outcome to obtain its scale-aware depth from dense depthestimation results calculated by our novel unsupervised RoboDepth model onlywith the monocular camera feedback, hence in return fusing the above real-time3D position into our control loop. To eliminate the misorientation of FOVcaused by Remote Center of Motion (RCM) constraints when moving thelaparoscope, we propose a novel distortion constraint using an affine map tominimize the visual warping problem, and a null-space controller is alsoembedded into the framework to optimize all types of errors in a unified anddecoupled manner. Experiments are conducted using Universal Robot (UR) and KarlStorz Laparoscope/Instruments, which prove the feasibility of our domainknowledge and learning enabled framework for automated camera control.

Data-driven Holistic Framework for Automated Laparoscope Optimal View Control with Learning-based Depth Perception