The stability of docking between a landing gangway and an offshore wind turbine landing platform under a wave impact directly influences personnel safety and work efficiency. To enhance docking stability, a laser point cloud and image fusion method for measuring the attitude of the offshore wind turbine landing gangway is proposed. A lidar and an industrial camera are assembled at the front end of the landing gangway to implement the proposed method. The proposed method consists of two main components: object detection and attitude estimation. In the object detection component, an improved YOLOv8 network and PointPillar network are introduced, incorporating a point cloud and image fusion branch for detecting offshore wind turbine landing platform objects. In the attitude estimation component, the color and geometric features of the target are integrated, and an objective function based on a distance weight function is designed. In addition, we propose an improved fast global registration (FGR) algorithm that uses integrated features and distance weighting. The experimental results demonstrate that under level 4 sea conditions, the proposed method achieves an attitude measurement error of less than 0.676°. The target detection algorithm improves detection accuracy by 13.8% compared to the classical PointPillar network, while the attitude measurement algorithm enhances attitude angle measurement accuracy by at least 7.9% relative to the classical FGR algorithm.