Abstract
Simultaneous Localization and Mapping (SLAM) has always been a research hotspot in the field of computer vision and robotics, which aims to predict the position of a robot and use various sensors to perceive its surrounding environment information to build a map and complete navigation. The technology of SLAM has been widely applied in different fileds, especially the autonomous underwater vehicles (AUVs). AUVs can replace humans in various dangerous operations and exploration work underwater to assist humans in underwater development and research. In the past 20 years, benifited from the rapid development of deep learning and SLAM, AUV developed rapidly and has become a mainstream oceanographic exploration tool. At present, due to the complexity of underwater environment, the research on the precision navigation of AUV based on SLAM is still an open issue. Taking China and the United States as examples, this paper first introduced the representative AUV-SLAM algorithm and its development. Secondly, this paper also quantitatively compares the performance of different underwater SLAM algorithms and analyzes their application difficulties. Finally, the future development trend of AUV-SLAM is discussed.
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