Abstract
With the rapid development of sensor and artificial intelligence (AI) technologies, intelligent surface vehicles (ISVs) have gained increasing attention from academia and industry. Their intelligence, reliability, and safety depend heavily on situational awareness in complex navigational environments. To achieve high-quality perception, we develop a vision-centered multi-sensor fusion system, named NavEYE, by exploiting complementary sensors, including the automatic identification system (AIS), radar, and RGB camera. Specifically, we first propose a multi-constrained gated data association method (MCGA) to accurately match low-temporal-resolution AIS data with high-temporal-resolution radar data. Their fusion result is then obtained by selectively implementing distance-aware adaptively weighted fusion (DAWF) and timeliness decay-based stitching fusion (TDSF), which reduce the uncertainty caused by AIS or radar data loss in real-world sensing scenarios. Based on accurate and robust visual object detection, we further associate and fuse AIS, radar, and visual data through joint constraints of normalized bearing and distance features. According to the fusion results, comprehensive information related to ships of interest can be automatically obtained, helping enhance situational awareness and reduce collision risk for ISVs. The feasibility, robustness, usability, and effectiveness of the proposed multi-sensor fusion method and situational awareness system are demonstrated through extensive experiments on a real-world sensing dataset collected from AIS, radar, and camera. The experimental results show the superior performance of our fusion method in both quantitative and qualitative evaluations. In addition, the shipboard NavEYE system can promote navigational safety for ISVs in complex and dynamic environments.
Abstract (translated)
URL
https://arxiv.org/abs/2607.03915
