Action planning of autonomous robots that take pictures for construction of real-world knowledge database

2015 Fiscal Year Final Research Report

• Project/Area Number: 26880004
• Research Category: Grant-in-Aid for Research Activity Start-up
• Allocation Type: Single-year Grants
• Research Field: Intelligent robotics
• Research Institution: The University of Tokyo
• Principal Investigator: Kanezaki Asako 東京大学, 情報理工学(系)研究科, 助教 (00738073)
• Project Period (FY): 2014-08-29 – 2016-03-31
• Keywords: ロボットビジョン / 三次元物体認識 / 物体検出

Outline of Final Research Achievements

First, we proposed an optimization method for estimating the parameters that typically appear in graph theoretical formulations of the matching problem for object detection. Although several methods have been proposed to optimize parameters for graph matching in a way to promote correct correspondences and to restrict wrong ones, our approach is novel in the sense that it aims at improving performance in the more general task of object detection. We presented this work at 3DV 2014 and also achieved 30th (2015) RSJ Young Investigation Excellence Award. Second, to detect unknown objects in the real world, we proposed a new method for obtaining object candidates in 3D space. Our method requires no learning, has no limitation of object properties such as compactness or symmetry, and therefore produces object candidates using a completely general approach. We presented this work at IROS 2015 and also published open source code.

Free Research Field

コンピュータビジョン，機械学習，物体認識