CCPose: high-precision six-dimensional pose estimation for industrial objects

Abstract

High-precision six-degree-of-freedom (6D) pose estimation of texture-less industrial objects is a critical capability for advancing industrial robotics, particularly in high-mix production environments. Existing methods often struggle with texture-less or reflective objects and lack the millimeter-level accuracy required for precise manipulation tasks. This paper introduces Center-and-Curvature Pose (CCPose), a novel approach that combines machine learning with classical optimization to address these challenges. CCPose operates through a three-stage process: (1) predicting center and curvature heatmaps using a fully convolutional neural network, (2) triangulating three-dimensional (3D) object centers from multi-view images, and (3) refining poses via a render-and-compare optimization. The method achieves state-of-the-art performance on the Texture-Less (T-LESS) dataset, significantly outperforming existing methods on metrics measuring 3D surface deviation. Additionally, the practical applicability of CCPose is demonstrated by the successful integration into a real-world robotic pick-and-place application, handling texture-less metal objects under various lighting conditions. The system generalizes well to unseen objects and provides interpretable outputs, facilitating data-driven improvements. This work represents a significant advancement in 6D pose estimation, offering a robust and precise solution for industrial automation.