A Piecewise-weighted RANSAC Method Utilizing Abandoned Hypothesis Model Information with a New Application on Robot Self-calibration

Industrial robots and collaborative robots are widely employed in industry and are progressively being utilized to assist individuals in their daily routines. To improve their absolute accuracy, self-calibration methods using portable local measurement devices are cost-effective solutions. However, compared with the conventional external calibration methods, self-calibration methods employing two configurations as a calibration sample introduce more non-kinematic errors to the robot. Therefore, noise reduction is significantly necessary in self-calibration. A novel Piecewise-weighted Random Sample Consensus (RANSAC) method is proposed in this paper. Instead of choosing an optimal model with all inliers, the proposed method employs a general weight considering both the sample and hypothesis model qualities to generate a new model with Weighted Least Square (WLS) method. Besides, the proposed method turns the target of finding an uncontaminated set of inliers into the training of the proper weight coefficient for WLS, which not only improves the accuracy but also greatly enhances the speed. The self-calibration experiment on a 6 degree-of-freedom(DOF) robot CR10 shows that the accuracy of the proposed Piecewise-weighted RANSAC method makes a 27.7% accuracy improvement from that employing Least Square method, a 20.0% accuracy improvement from that employing standard RANSAC method, and a 5.5% accuracy improvement from that employing LO-RANSAC method. Besides, the proposed method is also over 10.9 times faster than the standard RANSAC method and 18.6 times faster than the LO-RANSAC method.