Abstract: This paper presents a new open-source dataset with ground truth position in a simulated colon environment to promote development of real-time feedback systems for physicians performing colonoscopies. Four systems (DSO, LSD-SLAM, SfMLearner, ORB-SLAM2) are tested on this dataset and their failures are analyzed. A data collection platform was fabricated and used to take the dataset in a colonoscopy training simulator that was affixed to a flat surface. The noise in the ground truth positional data induced from the metal in the data collection platform was then characterized and corrected. The Absolute Trajectory RMSE Error (ATE) and Relative Error (RE) metrics were performed on each of the sequences in the dataset for each of the Simultaneous Localization And Mapping (SLAM) systems. While these systems all had good performance in idealized conditions, more realistic conditions in the harder sequences caused them to produce poor results or fail completely. These failures will be a hindrance to physicians in a real-world scenario, so future systems made for this environment must be more robust to the difficulties found in the colon, even at the expense of trajectory accuracy. The authors believe that this is the first open-source dataset with groundtruth data displaying a simulated in vivo environment with active deformation, and that this is the first step toward achieving useful SLAM within the colon. 

Fulton, M.J., Prendergast, J.M., DiTommaso, E.R., Rentschler, M.E., “Comparing Visual Odometry Systems in Actively Deforming Simulated Colon Environments,”  IEEE International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, October, 2020.

(Dataset, Video, IROS Presentation)