  Laparoscopic Field of View (FOV) control is one of the most fundamental and
important components in Minimally Invasive Surgery (MIS), nevertheless, the
traditional manual holding paradigm may easily bring fatigue to surgical
assistants, and misunderstanding between surgeons also hinders assistants to
provide a high-quality FOV. Targeting this problem, we here present a
data-driven framework to realize an automated laparoscopic optimal FOV control.
To achieve this goal, we offline learn a motion strategy of laparoscope
relative to the surgeon's hand-held surgical tool from our in-house surgical
videos, developing our control domain knowledge and an optimal view generator.
To adjust the laparoscope online, we first adopt a learning-based method to
segment the two-dimensional (2D) position of the surgical tool, and further
leverage this outcome to obtain its scale-aware depth from dense depth
estimation results calculated by our novel unsupervised RoboDepth model only
with the monocular camera feedback, hence in return fusing the above real-time
3D position into our control loop. To eliminate the misorientation of FOV
caused by Remote Center of Motion (RCM) constraints when moving the
laparoscope, we propose a novel distortion constraint using an affine map to
minimize the visual warping problem, and a null-space controller is also
embedded into the framework to optimize all types of errors in a unified and
decoupled manner. Experiments are conducted using Universal Robot (UR) and Karl
Storz Laparoscope/Instruments, which prove the feasibility of our domain
knowledge and learning enabled framework for automated camera control.
