Abstract

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 \(640 \times 480\) ). 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. Code is available at https://github.com/JingweiSong/BDIS-SLAM.

中文翻译：

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BDIS-SLAM：用于手术的基于 CPU 的轻量级密集立体 SLAM

摘要

目的

微创手术（MIS）中常见的密集立体同步定位和建图（SLAM）方法需要高端并行计算资源才能实时实现。然而，这并不总是可行，因为计算资源应该分配给其他任务，如分割、检测和跟踪。为了解决并行计算能力有限的问题，本研究针对一种轻量级密集立体SLAM系统，该系统在单核CPU上工作并实现实时性能（典型场景超过30 Hz）。

方法

ORB-SLAM2系统集成了一种新的密集立体映射模块，命名为BDIS-SLAM。我们新的密集立体映射模块包括立体匹配和 3D 密集深度镶嵌方法。立体匹配是通过最近提出的CPU级实时匹配算法贝叶斯密集逆搜索（BDIS）来实现的。基于 BDIS 的形状恢复和深度镶嵌策略集成为新线程，并与骨干 ORB-SLAM2 系统相结合，以实现实时立体形状恢复。

结果

体内数据集实验表明，在典型的内窥镜/结肠镜检查场景中，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 仅消耗大约\(12\%\) 的额外时间。尽管我们的轻量级 BDIS-SLAM 通过忽略变形和融合程序来简化流程，但它可以为计算受限设备上的现代 MIS 提供可用的密集映射。

结论

所提出的 BDIS-SLAM 是一种用于 MIS 的轻量级立体密集 SLAM 系统。在典型的内窥镜/结肠镜检查场景（图像大小约为\(640 \times 480\) ）中，它在现代单核 CPU 上实现了 30 Hz 。BDIS-SLAM为MIS中的密集建图提供了低成本的解决方案，并具有在手术机器人和AR系统中应用的潜力。代码可在 https://github.com/JingweiSong/BDIS-SLAM 获取。