Pose Estimation of Stacked Rectangular Objects from Depth Images

This paper addresses the task of six degrees of freedom (6-DoF) pose estimation of stacked rectangular objects from depth images. Object pose estimation is one of the key challenges for visual processing systems since it plays a vital role in many situations such as warehouse/factory automation, robotic manipulation, and augmented reality. Many recent approaches to object pose estimation use RGB information for detecting and estimating the pose of objects. However, in warehouse/factory automation, objects are often small, occluded, cluttered, and texture-less which makes it difficult to utilize RGB features for detection and pose estimation. In order to overcome this restriction, we use only the depth information (without RGB information) and its geometric features to segment each object and to estimate the 6-DoF (position and orientation) in a stacked scene. We segment the rectangular objects in each scene from the depth and surface normal discontinuities (geometric segmentation). From the geometrically segmented image, four object corner points can be estimated using the convex hull detection and the eight corner points, which are required for the 6-DoF pose estimation, can be calculated. To improve the accuracy of orientation estimation, we estimate four orientation candidates and select the best among them. Experimental results using two evaluation methods show that our method outperformed the baseline method.