  Deep learning techniques hold promise to develop dense topography
reconstruction and pose estimation methods for endoscopic videos. However,
currently available datasets do not support effective quantitative
benchmarking. In this paper, we introduce a comprehensive endoscopic SLAM
dataset consisting of 3D point cloud data for six porcine organs, capsule and
standard endoscopy recordings as well as synthetically generated data. A Panda
robotic arm, two commercially available capsule endoscopes, two conventional
endoscopes with different camera properties, and two high precision 3D scanners
were employed to collect data from 8 ex-vivo porcine gastrointestinal
(GI)-tract organs. In total, 35 sub-datasets are provided with 6D pose ground
truth for the ex-vivo part: 18 sub-dataset for colon, 12 sub-datasets for
stomach and 5 sub-datasets for small intestine, while four of these contain
polyp-mimicking elevations carried out by an expert gastroenterologist.
Synthetic capsule endoscopy frames from GI-tract with both depth and pose
annotations are included to facilitate the study of simulation-to-real transfer
learning algorithms. Additionally, we propound Endo-SfMLearner, an unsupervised
monocular depth and pose estimation method that combines residual networks with
spatial attention module in order to dictate the network to focus on
distinguishable and highly textured tissue regions. The proposed approach makes
use of a brightness-aware photometric loss to improve the robustness under fast
frame-to-frame illumination changes. To exemplify the use-case of the EndoSLAM
dataset, the performance of Endo-SfMLearner is extensively compared with the
state-of-the-art. The codes and the link for the dataset are publicly available
at https://github.com/CapsuleEndoscope/EndoSLAM. A video demonstrating the
experimental setup and procedure is accessible through
https://www.youtube.com/watch?v=G_LCe0aWWdQ.
