Robot motion visual measurement based on RANSAC and weighted constraints method

Abstract

Precision of the coordinate transformation model parameters is crucial for the accuracy of the vision-based robot spatial motion measurement method. In this work, an optimization algorithm integrating RANSAC and iterative weighted singular value decomposition (IR-SVD) is proposed for improving the coordinate transformation model solution precision, aiming at enhancing the spatial motion measurement accuracy of the binocular vision system. Considering noises existing in reference transformation point pairs, the RANSAC algorithm is introduced to filter the raw measurement point pairs and extract inliers, thereby eliminating potential gross errors and realizing the cluster of advantageous points. An enhanced SVD method based on iterative weighted constraints is proposed to substitute traditional SVD. After calculating the coordinate transformation model parameters, the measurement errors of inliers are solved synchronously, and the weights are reallocated in light of the measurement errors value, subsequently recalculating the coordinate transformation model parameters repeatedly until the errors converge. The validation experiments are conducted on the self-built three-degree-of-freedom rectangular coordinate robot platform. The experimental results of discrete point-to-point motion and continuous trajectory motion measurement show that the proposed method can improve the coordinate transformation model parameters solution accuracy effectively, comparing with the traditional SVD method. Comparative experiment with existing commonly used coordinate transformation methods including Quaternion and iterative closest point indicates that the proposed method exhibits the best applicability and minimal errors in robot motion visual measurement. Both accuracy of the coordinate transformation model solution and the visual system’s motion measurement are enhanced with this newly-proposed, optimized method.