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 will be available upon acceptance.

翻译：我们提出了一种拓扑建图与定位系统，能够在真实人体结肠镜检查中运行，尽管存在显著的形变与光照变化。该地图采用图结构表示，其中每个节点通过一组真实图像编码结肠位置，边则代表节点间的可通行性。针对时间邻近图像（场景变化较小），基于最新Transformer的局部特征匹配算法可成功实现地点识别。然而在长期变化场景下（如同患者不同次结肠镜检查），基于特征的匹配会失效。为解决此问题，我们在真实结肠镜图像上训练了深度全局描述符，在场景发生显著变化时仍能实现高召回率。结合贝叶斯滤波器可提升长期地点识别的精度，从而实现在预先构建的地图中重新定位。实验表明，ColonMapper能在两个重要用例中自主构建地图并实现定位：同次结肠镜检查内的定位，以及同一患者不同次结肠镜检查间的定位。相关代码将在论文录用后公开。