Monocular visual simultaneous localization and mapping (V-SLAM) is nowadays
an irreplaceable tool in mobile robotics and augmented reality, where it
performs robustly. However, human colonoscopies pose formidable challenges like
occlusions, blur, light changes, lack of texture, deformation, water jets or
tool interaction, which result in very frequent tracking losses. ORB-SLAM3, the
top performing multiple-map V-SLAM, is unable to recover from them by merging
sub-maps or relocalizing the camera, due to the poor performance of its place
recognition algorithm based on ORB features and DBoW2 bag-of-words.
We present CudaSIFT-SLAM, the first V-SLAM system able to process complete
human colonoscopies in real-time. To overcome the limitations of ORB-SLAM3, we
use SIFT instead of ORB features and replace the DBoW2 direct index with the
more computationally demanding brute-force matching, being able to successfully
match images separated in time for relocation and map merging. Real-time
performance is achieved thanks to CudaSIFT, a GPU implementation for SIFT
extraction and brute-force matching.
We benchmark our system in the C3VD phantom colon dataset, and in a full real
colonoscopy from the Endomapper dataset, demonstrating the capabilities to
merge sub-maps and relocate in them, obtaining significantly longer sub-maps.
Our system successfully maps in real-time 88 % of the frames in the C3VD
dataset. In a real screening colonoscopy, despite the much higher prevalence of
occluded and blurred frames, the mapping coverage is 53 % in carefully explored
areas and 38 % in the full sequence, a 70 % improvement over ORB-SLAM3.

CudaSIFT-SLAM 是首个能够实时处理完整人类结肠镜检测的 V-SLAM 系统，通过使用 SIFT 特征替代 ORB 特征以及更计算密集的暴力匹配算法，成功地解决了 ORB-SLAM3 的限制，并能够在时间上匹配图像以便重定位和地图融合。

CudaSIFT-SLAM：针对真人内窥镜手术全过程建图的多地图视觉 SLAM

CudaSIFT-SLAM: multiple-map visual SLAM for full procedure mapping in  real human endoscopy

Mapping and localization in endoluminal cavities from colonoscopies or
gastroscopies has to overcome the challenge of significant shape and
illumination changes between reobservations of the same endoluminal location.
Instead of geometrical maps that strongly rely on a fixed scene geometry,
topological maps are more adequate because they focus on visual place
recognition, i.e. the capability to determine if two video shots are imaging
the same location. We propose a topological mapping and localization system
able to operate on real human colonoscopies. The map is a graph where each node
codes a colon location by a set of real images of that location. The edges
represent traversability between two nodes. For close-in-time images, where
scene changes are minor, place recognition can be successfully managed with the
recent transformers-based image-matching algorithms. However, under long-term
changes --such as different colonoscopies of the same patient-- feature-based
matching fails. To address this, we propose a GeM global descriptor able to
achieve high recall with significant changes in the scene. The addition of a
Bayesian filter processing the map graph boosts the accuracy of the long-term
place recognition, enabling relocalization in a previously built map. In the
experiments, we construct a map during the withdrawal phase of a first
colonoscopy. Subsequently, we prove the ability to relocalize within this map
during a second colonoscopy of the same patient two weeks later. Code and
models will be available upon acceptance.

该论文提出一种基于拓扑地图的肠镜内部位置映射和定位方法，它能够通过使用节点表示肠道位置和边缘表示节点之间可穿越性的方式来操作真实的人类肠镜。此外，论文还提出一种全局描述符方法，该方法能够在长时间变化下实现高召回率的肠道位置识别，并结合贝叶斯过滤器对地图图形进行处理，从而提高了对先前建立地图的再定位的精度。