3D irregular object recognition for twist-lock handling system

The handling of twist-locks has been a heavy burden for the container industry. There have been many efforts in developing automated twist-lock handling solutions. To address this challenge, we are developing a customized mobile manipulator for twist-lock grasping. The technical challenge is 3D irregular object recognition in unstructured port environment. In this paper, we use PCA and KPCA to do two-level object recognition only depending on depth information to determine the basic instance and pose information for twist-lock grasping. The extensive experiments are carried out to select the optimal recognition parameters, investigate the performance of PCA, KPCA and compare their performance. Since depth images are insensitive to changes in lighting conditions, the experimental results show that the proposed approach based on depth information is effective to address the issues and solve problems caused by rust and painting peeled off of twist-lock handling in unstructured port environment.