Vessel trajectory prediction plays a crucial role in ensuring the safety and efficiency of maritime transportation. This study proposes an innovative sequence-to-sequence model, called the Vessel Influence Long Short-Term Memory (VI-LSTM), which introduces a novel Vessel Influence Map (VIM) to quantitatively model the dynamic effects of surrounding vessels. To enhance reliability, VI-LSTM incorporates Gaussian distribution predictions combined with Monte Carlo dropout techniques to estimate prediction uncertainty. Additionally, a temporally weighted hybrid loss function is designed to balance prediction accuracy and uncertainty. Furthermore, this study systematically categorizes and models factors influencing vessel trajectory prediction. Experimental results demonstrate that VI-LSTM achieves a mean distance error of 330.66 m on the standard test set and 480.30 m on an unseen subject test set, outperforming other comparative models, particularly in complex navigation scenarios and high-density maritime environments. These innovations significantly improve the accuracy and generalizability of vessel trajectory predictions, leading to enhanced safety, increased efficiency, and more effective collision avoidance in maritime navigation.

Current lidar-inertial SLAM algorithms mainly rely on the geometric features of the lidar for point cloud alignment. The issue of incorrect feature association arises because the matching process is susceptible to influences such as dynamic objects, occlusion, and environmental changes. To address this issue, we present a lidar-inertial SLAM system based on the LIO-SAM framework, combining semantic and geometric constraints for association optimization and keyframe selection. Specifically, we mitigate the impact of erroneous matching points on pose estimation by comparing the consistency of normal vectors in the surrounding region. Additionally, we incorporate semantic information to establish semantic constraints, further enhancing matching accuracy. Furthermore, we propose an adaptive selection strategy based on semantic differences between frames to improve the reliability of keyframe generation. Experimental results on the KITTI dataset indicate that, compared to other systems, the accuracy of the pose estimation has significantly improved.