Abstract
We present a flexible system that can scan and localize work pieces in 3D for assembly and pick and place operations. The system contains a vision robot that acquires 3D point clouds by performing sweeps with a laser triangulation sensor. Because the sensor is mounted on a robot, we can choose a viewpoint and a scanner trajectory that is optimal for a given task. For example, we can sweep along a semi-circular trajectory in order to recognize and grasp parts from a pallet. With the same vision robot, we can perform a sweep of the packaging container in order to recognize and manipulate elements of cardboard.
The system also incorporates software that recognizes and localizes objects based on an acquired 3D point cloud and a CAD model of the object to search for. The core matching algorithm is based on oriented point pairs and a Hough-like voting scheme. The method has been improved with a robust clustering algorithm as well as methods for pose verification and pose refinement that significantly increase the accuracy and robustness of the system.
As an application of the system, an industrial prototype work cell is presented. The task is to recognize, grasp and transfer parts of office chairs, such as seats, seat backs and armrests, from a pallet to a cardboard container. It demonstrates how the vision system is easily set up to recognize three different components by using CAD models. The prototype work cell further demonstrates how the pose estimation results can be fed directly to a separate handling robot in order to grasp a chair seat. A series of ten experiments was performed where the chair seat was placed in arbitrary poses in the pallet. Pose estimations were performed in just over one second per experiment, and the obtained accuracy was well within the tolerances for the grasp operation in all ten cases.
The system also incorporates software that recognizes and localizes objects based on an acquired 3D point cloud and a CAD model of the object to search for. The core matching algorithm is based on oriented point pairs and a Hough-like voting scheme. The method has been improved with a robust clustering algorithm as well as methods for pose verification and pose refinement that significantly increase the accuracy and robustness of the system.
As an application of the system, an industrial prototype work cell is presented. The task is to recognize, grasp and transfer parts of office chairs, such as seats, seat backs and armrests, from a pallet to a cardboard container. It demonstrates how the vision system is easily set up to recognize three different components by using CAD models. The prototype work cell further demonstrates how the pose estimation results can be fed directly to a separate handling robot in order to grasp a chair seat. A series of ten experiments was performed where the chair seat was placed in arbitrary poses in the pallet. Pose estimations were performed in just over one second per experiment, and the obtained accuracy was well within the tolerances for the grasp operation in all ten cases.
