Underwater ice adaptive mapping and reconstruction using autonomous underwater vehicles - Fan, Shuangshuang; Zhang, Xinyu; Zeng, Guangxian; Cheng, Xiao (Frontiers in Marine Science, 2023)
The undersides of floating ice shelves and sea ice in the Antarctic and Arctic are among the least accessible environments on Earth. The interactions between ice shelves, sea ice and the ocean are of considerable scientific interest. In order to fully understand the complex picture of sea ice, apart from its surface, it is quite necessary to map the underside to comprehend the full context of its growth and decay patterns. Autonomous Underwater Vehicle (AUV) are rapidly becoming the desired platform of choice for mapping the underside of sea ice to provide high resolution 3D view of sea ice topography to date. To increase the efficiency and accuracy of AUV sampling behaviors is significant for the under-ice observation mission given its limited endurance. In this paper, we present a low-cost underwater ice mapping framework for small-sized AUVs using adaptive sampling and map reconstruction methods. A small-sized AUV is cost-effective and convenient for operation in polar regions; but with limited loading capacity and energy, it is applicable for the vehicle to carry single-beam sonar for ice bottom mapping but not the multi-beam one. Thus, the essential issue in this application is how to obtain the key information of ice topography and how to reconstruct the map of ice draft (namely underwater ice thickness) with AUV sparse mapping swathes. To address this, we propose a graphics-based adaptive mapping method to densify the measuring of ice bottom surface with ‘noticeable’ variations; moreover, we also present a spare approximation method for ice draft map reconstruction using the sparse mapping swathes from a single-beam sonar. Our efforts are to introduce an effective and efficient approach for underwater ice mapping using low-cost small-sized AUVs. Our proposed adaptive mapping and reconstruction methods are validated in the under-ice scenario created using the field data.