A survey on maritime perception datasets and technologies for autonomous surface vessels

Abstract

Abstract In recent years, maritime autonomous surface vessels (ASVs) have garnered increasing attention due to advancements in sensor technologies and deep learning. A key challenge in developing safe maritime vessel navigation is the ability to perceive the dynamic maritime environment, which involves tasks such as obstacle detection, recognition of navigational elements, and situational awareness. This paper reviews a range of studies on maritime datasets and perception algorithms, many of which present their own datasets alongside perception models. The datasets are organized based on sensor configurations and task objectives, as well as the algorithms used for tasks such as object detection, semantic segmentation, target tracking, multimodal sensor fusion, and simultaneous localization and mapping (SLAM). This survey provides a comprehensive overview of recent advancements in maritime perception from a robotics perspective and offers valuable insights to guide future research toward the development of safe and reliable autonomous ship navigation systems.