Title: A 3-Dimensional Object Recognition Method Using Relationship of Distances and Angles in Feature Points

Year of Publication: Dec - 2015
Page Numbers: 137-147
Authors: Seiichi Maehara, Kazuo Ikeshiro, Hiroki Imamura
Conference Name: The Second International Conference on Digital Information Processing, Data Mining, and Wireless Communications (DIPDMWC2015)
- United Arab Emirates

Abstract:


In recent years, human support robots have been receiving attention. Especially, objects recognition task is important in case that people request the robots to transport and rearrange an object. We consider that there are five necessary properties to recognize in domestic environment as follows.  Robustness against occlusion  Fast recognition  Pose estimation with high accuracy  Coping with erroneous correspondences  Recognizing objects in different aspect ratio As conventional object recognition methods using 3-dimensional information, there are model-based recognition methods such as the SHOT and the Spin Image. The SHOT and the Spin Image do not satisfy all five properties for the robots. Therefore, to satisfy the five properties of recognition, we propose a 3-dimensional object recognition method by using relationships of distances and angles in feature points. As our approaches, firstly, the proposed method uses a curvature as a feature in a local region. Secondly, the proposed method uses points having high curvature as feature points. Finally, the proposed method generates a list by listing relationship of distances and angles between feature points and matches lists.