A Framework for 3D Object Detection and Pose Estimation in Unstructured Environment Using Single Shot Detector and Refined LineMOD Template Matching

Abstract

In order to improve the robot’s perception ability in the complicated environment, especially the unstructured environment, a framework of 3D object detection and pose estimation using single shot detector (SSD) and modified LineMOD template matching is proposed, which can detect multiple objects and estimate their pose simultaneously. Firstly, the initial object detection (the first detection) is realized by single shot detector network and therefore the region of interest (RoI) of target objects are generated. LineMOD template matching is then applied to provide candidate templates. These calculated templates are grouped by the designed clustering algorithm. After sorting the clusters according to the descending order of the average similarity, non-maximum suppression removes the similar results and provide the further multiple detection results (the second detection). Finally, based on the results from the second detection, the pose of the object is estimated by using iterative closest point (ICP) algorithm. The object detection experiments show that on Tejani dataset, the average recognition rate of six objects reaches 99.25%. For the object pose estimation, F1 of the proposed method is 21.7% higher than the conventional method in the pose estimation experiments. Also, F1 of the presented algorithm is 9.5% higher than Deep-6Dpose method. Both comparison experiments verify the effectiveness of the proposed framework. Further, this framework for object detection and pose estimation is employed to do robotic grasping. In particular, the workpiece of steel plates is grabbed, which is a necessary procedure of the polishing technique.