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Purpose: Common dense stereo Simultaneous Localization and Mapping (SLAM)approaches in Minimally Invasive Surgery (MIS) require high-end parallelcomputational resources for real-time implementation. Yet, it is not alwaysfeasible since the computational resources should be allocated to other taskslike segmentation, detection, and tracking. To solve the problem of limitedparallel computational power, this research aims at a lightweight dense stereoSLAM system that works on a single-core CPU and achieves real-time performance(more than 30 Hz in typical scenarios). Methods: A new dense stereo mappingmodule is integrated with the ORB-SLAM2 system and named BDIS-SLAM. Our newdense stereo mapping module includes stereo matching and 3D dense depth mosaicmethods. Stereo matching is achieved with the recently proposed CPU-levelreal-time matching algorithm Bayesian Dense Inverse Searching (BDIS). ABDIS-based shape recovery and a depth mosaic strategy are integrated as a newthread and coupled with the backbone ORB-SLAM2 system for real-time stereoshape recovery. Results: Experiments on in-vivo data sets show that BDIS-SLAMruns at over 30 Hz speed on modern single-core CPU in typicalendoscopy/colonoscopy scenarios. BDIS-SLAM only consumes around an additional12% time compared with the backbone ORB-SLAM2. Although our lightweightBDIS-SLAM simplifies the process by ignoring deformation and fusion procedures,it can provide a usable dense mapping for modern MIS on computationallyconstrained devices. Conclusion: The proposed BDIS-SLAM is a lightweight stereodense SLAM system for MIS. It achieves 30 Hz on a modern single-core CPU intypical endoscopy/colonoscopy scenarios (image size around 640*480). BDIS-SLAMprovides a low-cost solution for dense mapping in MIS and has the potential tobe applied in surgical robots and AR systems.

BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgery