arXiv 2312.156792023-12-25cs.ROcs.CV

BDIS-SLAM：一种用于手术的轻量级基于CPU的稠密立体SLAM

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

United Imaging Research Institute of Intelligent Imaging（联影智能影像研究院）；University of Michigan（密歇根大学）；University of Technology Sydney（悉尼科技大学）；Imperial College London（伦敦帝国理工学院）

Jingwei Song, Ray Zhang, Qiuchen Zhu, Jianyu Lin, Maani Ghaffari

备注：: This paper has been accepted by International Journal of Computer Assisted Radiology and Surgery. Code is available at https://github.com/JingweiSong/BDIS-SLAM

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AI总结：

针对微创手术中计算资源受限的问题，提出基于单核CPU的轻量级稠密立体SLAM系统BDIS-SLAM，集成BDIS匹配算法，在典型场景下实现30 Hz以上实时稠密建图。

摘要：

目的：在微创手术（MIS）中，常见的稠密立体同步定位与建图（SLAM）方法需要高性能的并行计算资源才能实现实时运行。然而，这并非总是可行的，因为计算资源需要分配给其他任务，如分割、检测和跟踪。为解决有限并行计算能力的问题，本研究旨在开发一种轻量级稠密立体SLAM系统，该系统可在单核CPU上运行并实现实时性能（在典型场景中超过30 Hz）。方法：将一个新的稠密立体建图模块集成到ORB-SLAM2系统中，命名为BDIS-SLAM。我们新的稠密立体建图模块包括立体匹配和3D稠密深度拼接方法。立体匹配通过最近提出的CPU级实时匹配算法——贝叶斯稠密逆搜索（BDIS）实现。基于BDIS的形状恢复和深度拼接策略作为一个新线程集成，并与主干ORB-SLAM2系统耦合，用于实时立体形状恢复。结果：在体内数据集上的实验表明，在典型的内窥镜/结肠镜场景中，BDIS-SLAM在现代单核CPU上以超过30 Hz的速度运行。与主干ORB-SLAM2相比，BDIS-SLAM仅额外消耗约12%的时间。尽管我们的轻量级BDIS-SLAM通过忽略变形和融合过程简化了流程，但它可以在计算受限的设备上为现代MIS提供可用的稠密建图。结论：所提出的BDIS-SLAM是一种用于MIS的轻量级立体稠密SLAM系统。在典型的内窥镜/结肠镜场景（图像尺寸约为640*480）中，它在现代单核CPU上达到30 Hz。BDIS-SLAM为MIS中的稠密建图提供了一种低成本解决方案，并有望应用于手术机器人和增强现实系统。

英文摘要：

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.

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