Circular-feature-based monocular pose measurement method for robotic assembly considering eccentricity

In robotic assembly, precise pose estimation of assembly components or tools via vision is critical to task success. To enhance the accuracy of the classical Perspective-n-Point (PnP) algorithm based on circular features, this paper proposes a high-precision monocular pose estimation method that accounts for circular eccentricity. First, based on monocular projection geometry, we derive an eccentricity parameterization model for single circular contours and an image-processing-based eccentricity error estimation model. Furthermore, by introducing an error weighting factor to explicitly incorporate eccentricity errors into the reprojection error of the PnP problem, we establish a new pose estimation optimization model and solution strategy, to the best of our knowledge. Experimental results demonstrate that compared to traditional PnP methods that rely solely on feature center points, the proposed method further leverages the rich geometric information of circular features. It effectively enhances pose estimation accuracy and numerical stability in the translation vector while reducing processing time. In assembly application experiments, the proposed algorithm enables smoother convergence of end-effector pose errors compared to traditional methods, significantly improving assembly stability during the process.