Point cloud method for identifying the center pose of the pin shaft end face in the automated dismantling of oil derricks

The derrick of oil drilling rigs is constructed by pin shafts connecting steel frames. Traditional manual disassembly methods pose safety risks and exhibit low efficiency. To achieve automated disassembly, precise identification of the end-face center poses (position and orientation) of pin shafts is essential. However, conventional registration and localization methods suffer from insufficient positioning accuracy due to incomplete point cloud acquisition of pin shafts, while existing deep learning approaches struggle to accurately calculate local object poses. To solve the above problem, this paper proposes a method that combines deep learning and traditional point cloud processing for the precise positioning of the center of the pin end face on the derrick of an oil rig. Specifically, a point cloud segmentation network based on PointNet++ is used in the deep learning part for effectively extracting the point cloud data of the pin shaft and its surrounding structures from the complex scene. In the point cloud processing part, an innovative method based on RANSAC plane fitting is proposed, in which a local coordinate system is established for the fitted plane, the attitude information is calculated, and the plane is sliced for extracting the center of the pin endface to realize the spatial localization of the pin endface.The experimental results show that the positioning error of the proposed method along the X/Y/Z axes is less than 0.5 mm, the directional error is less than 1°, and the accuracy is as high as 96%, which is better than that of the traditional method in terms of precision and accuracy. This study provides a high-precision pin shaft localization solution for automated derrick disassembly, effectively mitigating safety risks and enhancing operational efficiency.