To address the limitations of traditional ICP (Iterative closest point) algorithms in low-overlap scenarios and under noisy interference, this paper proposes an improved ICP point cloud registration method based on an errorguided threshold adjustment mechanism. The approach aims to enhance the accuracy and robustness of point cloud registration. During the coarse registration stage, fast point feature histograms (FPFH) are combined with the random sample consensus (RANSAC) algorithm. By employing random sampling and introducing a triangle similarity constraint, distinctive corresponding point pairs are selected to estimate the initial pose transformation between point clouds. In the fine registration stage, an error-guided threshold adjustment mechanism dynamically updates the distance threshold based on the matching error in each iteration. This ensures that each point in the source point cloud is matched only to its nearest point within the adaptive threshold in the target point cloud, thereby effectively filtering out invalid correspondences. The proposed method is validated on multiple public point cloud datasets, including models with complex geometric structures and large-scale scenes. Experimental results demonstate that the method significantly improves registration accuracy and maintains robust performance even under challenging conditions such as low overlap and high noise levels.