Three-dimensional pose estimation model for object with complex surface

The proposed three-dimensional pose estimation model for object with complex surface, which primarily absorbs the essence of scale-invariant feature transform and iterative closest point algorithm, includes two steps, off-line and online. At first, two kinds of feature databases are established in the off-line operations. Then, the online process mainly has three steps. The first one is two-dimensional edge extraction from red–green–blue (RGB) information based on scale-invariant feature transform algorithm. The second one is three-dimensional surface reconstruction from the previous two-dimensional edge and the depth information obtained from depth camera. The last one is three-dimensional pose estimation based on camera calibration and iterative closest point algorithm. The Kinect camera is selected as the information acquisition device which can produce red–green–blue information and depth information. In the experiment, the container twist-lock with complex surface is taken as the object. The result shows that the accuracy of the proposed model is very high