Tank dataset: An underwater multi-sensor dataset for SLAM evaluation

Underwater visual Simultaneous Localization and Mapping (SLAM) is essential for autonomous underwater navigation and close-range underwater inspection. However, the turbid and low-light conditions common underwater severely limit visibility and cause motion blurring, posing significant open challenges for visual SLAM approaches deployed underwater. On the other hand, the scarcity of public underwater multi-sensor datasets, coupled with the lack of 6-Degree-of-Freedom (6-DoF) ground truth data for SLAM evaluation, hinders the advancement of underwater visual SLAM research. To address these problems, this paper introduces an underwater dataset encompassing multi-sensor data from a stereo camera, an Inertial Measurement Unit, a Doppler Velocity Log, and a pressure sensor. To cover various difficulty levels for underwater SLAM evaluation, it provides eight sequences collected under different speed and illumination conditions. Extrinsic and intrinsic calibration parameters are also provided for multi-sensor fusion. Additionally, we present TankGT, a fiducial-marker-based SLAM system designed to provide highly accurate 6-DoF ground truth poses in underwater environments, enabling rigorous quantitative and qualitative benchmarking for underwater SLAM algorithms. We demonstrate the effectiveness of the proposed Tank dataset with four SLAM algorithms. The dataset is released to facilitate underwater SLAM research in the community at https://senseroboticslab.github.io/underwater-tank-dataset .