Three-dimensional contour detection method based on fusion of machine vision and laser radar

In the current methods of point cloud processing, there are still several limitations, particularly in achieving high precision and accuracy for large objects in complex environments. Existing techniques often struggle with incomplete or noisy data, leading to inaccurate contour extraction. In view of the challenges associated with the sparse and discrete nature of point clouds in complex environments, which lead to poor accuracy and stability in object contour extraction, this paper proposes a novel method for accurately extracting the contours of three-dimensional target point clouds. The method integrates high-resolution images with sparse point cloud information to address these issues. Firstly, the local characteristics of the point cloud are calculated, allowing for the selection of a contour point cloud. Next, depth information from two-dimensional images is obtained through a fuzzy mapping relationship. Finally, constraint conditions are established to derive a more accurate predicted value of the contour point cloud. Experiments demonstrate that the proposed method effectively improves the precision and accuracy of contour extraction for large objects, reducing measurement deviation by approximately 64.9% compared to using the original point cloud alone. Additionally, the method shows a more accurate completion effect on parts of the contour that are missing, underscoring its robustness and effectiveness in challenging scenarios.