BDIS-SLAM: A lightweight CPU-based dense stereo SLAM for surgery
Jingwei Song Ray Zhang Qiuchen Zhu Jianyu Lin Maani Ghaffari
Purpose: Common dense stereo Simultaneous Localization and Mapping (SLAM)
approaches in Minimally Invasive Surgery (MIS) require high-end parallel
computational resources for real-time implementation. Yet, it is not always
feasible since the computational resources should be allocated to other tasks
like segmentation, detection, and tracking. To solve the problem of limited
parallel computational power, this research aims at a lightweight dense stereo
SLAM 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 mapping
module is integrated with the ORB-SLAM2 system and named BDIS-SLAM. Our new
dense stereo mapping module includes stereo matching and 3D dense depth mosaic
methods. Stereo matching is achieved with the recently proposed CPU-level
real-time matching algorithm Bayesian Dense Inverse Searching (BDIS). A
BDIS-based shape recovery and a depth mosaic strategy are integrated as a new
thread and coupled with the backbone ORB-SLAM2 system for real-time stereo
shape recovery. Results: Experiments on in-vivo data sets show that BDIS-SLAM
runs at over 30 Hz speed on modern single-core CPU in typical
endoscopy/colonoscopy scenarios. BDIS-SLAM only consumes around an additional
12% time compared with the backbone ORB-SLAM2. Although our lightweight
BDIS-SLAM simplifies the process by ignoring deformation and fusion procedures,
it can provide a usable dense mapping for modern MIS on computationally
constrained devices. Conclusion: The proposed BDIS-SLAM is a lightweight stereo
dense SLAM system for MIS. It achieves 30 Hz on a modern single-core CPU in
typical endoscopy/colonoscopy scenarios (image size around 640*480). BDIS-SLAM
provides a low-cost solution for dense mapping in MIS and has the potential to
be applied in surgical robots and AR systems.