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

RGB-D Semantic SLAM for Surgical Robot Navigation in the Operating Room