  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.
