3D object recognition and pose estimation for random bin-picking using Partition Viewpoint Feature Histograms

摘要

3D object recognition and pose estimation are challenging tasks in industrial scenarios. In this paper, we propose an accurate and robust algorithm for object recognition and 6DOF pose estimation for bin-picking applications. We first split the whole point cloud of the object into four parts according to the bounding box of the point cloud. The surface shape characteristics are extracted from these four parts using an extended fast point feature histogram and an extended viewpoint direction component. These surface shape characteristics of four parts are further concatenated to generate a Partition Viewpoint Feature Histogram (PVFH) descriptor. Comparison with the state-of-the-art global descriptors have demonstrated the effectiveness of PVFH. Experimental results on industrial parts demonstrate that the PVFH feature descriptor ensures more accurate pose estimation, and higher computational efficiency. (C) 2019 Elsevier B.V. All rights reserved.