Simultaneous Place and Object Recognition with Mobile Robot Using Pose Encoded Contextual Information
Place and object recognition are two fundamental problems for mobile robot to understand its surroundings. In the field of computer vision it has been acknowledged that context plays an important role in image parsing, but in most of the researches contextual information is only used in one direction and little attention is paid to the relative pose context between objects and local features. We observe, however, place and object can serve as context to each other, that is the recognition of one facilitates the recognition of the other. In this paper, a new hierarchical random field which can encode multiple kinds of context including co-occurrence context, temporal context and relative pose context is proposed for simultaneous place and object recognition with a mobile platform. And a new kind of relative pose context, which is scale and rotation invariant, is defined to improve the stability of pose-encoded context. Experimental results with a mobile robot prove that the proposed method significantly improve the precision of the place and object recognition in familiar and unfamiliar environments.
Ronghua Luo Songhao Piao Huaqing Min
School of Computer Science and Engineering,South China University of Technology,510006,Guangzhou,Chi School of Computer Science and Technology,Harbin Institute of Technology,150001,Harbin,China School of Software,South China University of Technology,510006,Guangzhou,China
国际会议
2011 IEEE International Conference on Robotics and Automation(2011年IEEE世界机器人与自动化大会 ICRA 2011)
上海
英文
2792-2797
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）