Aiming at the problem of robotic picking in the scenario of stacked cluttered objects, an unordered picking system from target screening, recognition to 6D pose estimation was established. The Locally Convex Connected Patches method was used to segment the stacked cluttered objects collected by Kinect V2 camera into separate subsets of point cloud, and the uppermost unshaded object was selected as the target to be captured by defining the capture fraction, so as to ensure that robot could grasp the object from top to bottom. According to the picking requirements of different kinds of objects, 3d targets are identified and grasping points are located based on matching similarity function. An object 6D pose estimation model is established by combining TEASER(Truncated least squares Estimation And SEmidefinite Relaxation) algorithm and ICP(Iterative Closest Point) algorithm to ensure accurate registration of target point cloud and model point cloud under low coincidence rate. Experiments of 6D pose estimation and robotic unordered picking are carried out on self-collected data. The results show that the proposed 6D pose estimation method can obtain the 6D pose of the target more quickly and accurately compared with several popular methods. The root mean square distance error is less than 3.3 mm and the root mean square angle error is less than 5.6°. The visual processing time is far less than the movement time of robot arm, and the whole process of robotic real-time grasping is accomplished in the actual scene.