A Novel Point-Cloud-Based Alignment Method for Shelling Tool Pose Estimation in Aluminum Electrolysis Workshop

In aluminum electrolysis workshops, real-time pose perception of shelling heads is crucial to process accuracy and equipment safety. However, due to high temperatures, smoke, dust, and metal obstructions, traditional pose estimation methods struggle to achieve high accuracy and robustness. At the same time, the continuous movement of the shelling head and the similar geometric structures around it make it hard to match point-clouds, which makes it even harder to track the position and orientation. In response to the above challenges, we propose a multi-stage optimization pose estimation algorithm based on point-cloud processing. This method is designed for dynamic perception tasks of three-dimensional components in complex industrial scenarios. First stage improves the accuracy of initial matching by combining a weighted 3D Hough voting and adaptive threshold mechanism with an improved FPFH feature matching strategy. In the second stage, by integrating FPFH and PCA feature information, a stable initial registration is achieved using the RANSAC-IA coarse registration framework. In the third stage, we designed an improved ICP algorithm that effectively improved the convergence of the registration process and the accuracy of the final pose estimation. The experimental results show that the proposed method has good robustness and adaptability in a real electrolysis workshop environment, and can achieve pose estimation of the shelling head in the presence of noise, occlusion, and complex background interference.