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Computer-assisted systems are becoming broadly used in medicine. In endoscopy, most research focuses on the automatic detection of polyps or other pathologies, but localization and navigation of the endoscope are completely performed manually by physicians. To broaden this research and bring spatial Artificial Intelligence to endoscopies, data from complete procedures is needed. This paper introduces the Endomapper dataset, the first collection of complete endoscopy sequences acquired during regular medical practice, making secondary use of medical data. Its main purpose is to facilitate the development and evaluation of Visual Simultaneous Localization and Mapping (VSLAM) methods in real endoscopy data. The dataset contains more than 24 hours of video. It is the first endoscopic dataset that includes endoscope calibration as well as the original calibration videos. Meta-data and annotations associated with the dataset vary from the anatomical landmarks, procedure labeling, segmentations, reconstructions, simulated sequences with ground truth and same patient procedures. The software used in this paper is publicly available., (© 2023. Springer Nature Limited.)

Contemporary endoscopic simultaneous localization and mapping (SLAM) methods accurately compute endoscope poses; however, they only provide a sparse 3-D reconstruction that poorly describes the surgical scene. We propose a novel dense SLAM method whose qualities are: 1) monocular, requiring only RGB images of a handheld monocular endoscope; 2) fast, providing endoscope positional tracking and 3-D scene reconstruction, running in parallel threads; 3) dense, yielding an accurate dense reconstruction; 4) robust, to the severe illumination changes, poor texture and small deformations that are typical in endoscopy; and 5) self-contained, without needing any fiducials nor external tracking devices and, therefore, it can be smoothly integrated into the surgical workflow. It works as follows. First, accurate cluster frame poses are estimated using the sparse SLAM feature matches. The system segments clusters of video frames according to parallax criteria. Next, dense matches between cluster frames are computed in parallel by a variational approach that combines zero mean normalized cross correlation and a gradient Huber norm regularizer. This combination copes with challenging lighting and textures at an affordable time budget on a modern GPU. It can outperform pure stereo reconstructions, because the frames cluster can provide larger parallax from the endoscope's motion. We provide an extensive experimental validation on real sequences of the porcine abdominal cavity, both in-vivo and ex-vivo. We also show a qualitative evaluation on human liver. In addition, we show a comparison with the other dense SLAM methods showing the performance gain in terms of accuracy, density, and computation time.