We propose a topological mapping and localization system able to operate on real human colonoscopies, despite significant shape and illumination changes. The map is a graph where each node codes a colon location by a set of real images, while edges represent traversability between nodes. For close-in-time images, where scene changes are minor, place recognition can be successfully managed with the recent transformers-based local feature matching algorithms. However, under long-term changes -- such as different colonoscopies of the same patient -- feature-based matching fails. To address this, we train on real colonoscopies a deep global descriptor achieving high recall with significant changes in the scene. The addition of a Bayesian filter boosts the accuracy of long-term place recognition, enabling relocalization in a previously built map. Our experiments show that ColonMapper is able to autonomously build a map and localize against it in two important use cases: localization within the same colonoscopy or within different colonoscopies of the same patient. Code: https://github.com/jmorlana/ColonMapper.

翻译：我们提出了一种能够在真实人类结肠镜检查中运行的拓扑建图与定位系统，该系统能够应对显著的形状与光照变化。该地图是一个图结构，其中每个节点通过一组真实图像编码结肠位置，而边表示节点间的可通行性。对于时间相近的图像（场景变化较小），基于近期Transformer的局部特征匹配算法可以成功实现场景识别。然而，在长期变化下（例如同一患者的不同结肠镜检查），基于特征的匹配方法会失效。为解决此问题，我们在真实结肠镜数据上训练了一个深度全局描述符，使其在场景发生显著变化时仍能实现高召回率。贝叶斯滤波器的加入进一步提升了长期场景识别的准确度，从而能够在已构建的地图中实现重定位。实验表明，ColonMapper能够在两个重要应用场景中自主构建地图并实现定位：在同一结肠镜检查内或在同一患者的不同结肠镜检查中进行定位。代码：https://github.com/jmorlana/ColonMapper。