MIMIR-UW: A Multipurpose Synthetic Dataset for Underwater Navigation and Inspection
Olaya Álvarez-Tuñón, Hemanth Kanner, Luiza Ribeiro Marnet, Huy Xuan Pham, Jonas le Fevre Sejersen, Yury Brodskiy, Erdal Kayacan
Abstract
This paper presents MIMIR-UW, a multipurpose underwater synthetic dataset for SLAM, depth estimation, and object segmentation to bridge the gap between theory and application in underwater environments. MIMIR-UW integrates three camera sensors, inertial measurements, and ground truth for robot pose, image depth, and object segmentation. The underwater robot is deployed within a pipe exploration scenario, carrying artificial lights that create uneven lighting, in addition to natural artefacts such as reflections from natural light and backscattering effects. Four environments totalling eleven tracks are provided, with various difficulties regarding light conditions or dynamic elements. Two metrics for dataset evaluation are proposed, allowing MIMIR-UW to be compared with other datasets. State-of-art methods on SLAM, segmentation and depth estimation are deployed and benchmarked on MIMIR-UW. Moreover, the dataset's potential for sim-to-real transfer is demonstrated by leveraging the segmentation and depth estimation models trained on MIMIR-UW in a real pipeline inspection scenario. To the best of the authors' knowledge, this is the first underwater dataset targeted for such a variety of methods. The dataset is publicly available online. https://github.com/remaro-network/MIMIR-UW/