Aiming to solve the problems of missing parts of the point cloud in existing methods and overcome the limitations of the internal connection form of the research object, a target workpiece positioning algorithm based on feature surface extraction and point cloud registration is proposed for a concave-convex workpiece. First, this study extracts and restores the feature surface with obvious geometric features of the workpiece and roughly estimates the pose of the workpiece according to its pose. Second, the improved iterative closest point (ICP) registration algorithm is used to further refine the location. Additionally, to improve the ICP registration algorithm, the description of global features and loss function are added to the local features to limit the registration direction. Finally, positioning experiments under different stacking degrees show that the workpiece positioning is accurate. Moreover, the experimental results show that the proposed algorithm is suitable for workpiece positioning under different stacking conditions and is applicable to the grasping and positioning of industrial robots.