  Gaining spatial awareness of the Operating Room (OR) for surgical robotic
systems is a key technology that can enable intelligent applications aiming at
improved OR workflow. In this work, we present a method for semantic dense
reconstruction of the OR scene using multiple RGB-D cameras attached and
registered to the da Vinci Xi surgical system. We developed a novel SLAM
approach for robot pose tracking in dynamic OR environments and dense
reconstruction of the static OR table object. We validated our techniques in a
mock OR by collecting data sequences with corresponding optical tracking
trajectories as ground truth and manually annotated 100 frame segmentation
masks. The mean absolute trajectory error is $11.4\pm1.9$ mm and the mean
relative pose error is $1.53\pm0.48$ degrees per second. The segmentation DICE
score is improved from 0.814 to 0.902 by using our SLAM system compared to
single frame. Our approach effectively produces a dense OR table reconstruction
in dynamic clinical environments as well as improved semantic segmentation on
individual image frames.
