Autonomous Sign Reading for Semantic Mapping
We consider the problem of automatically collecting semantic labels during robotic mapping by extending the mapping system to include text detection and recognition modules. In particular, we describe a system by which a SLAMgenerated map of an office environment can be annotated with text labels such as room numbers and the names of office occupants. These labels are acquired automatically from signs posted on walls throughout a building. Deploying such a system using current text recognition systems, however, is dif- ficult since even state-of-the-art systems have difficulty reading text from non-document images. Despite these difficulties we present a series of additions to the typical mapping pipeline that nevertheless allow us to create highly usable results. In fact, we show how our text detection and recognition system, combined with several other ingredients, allows us to generate an annotated map that enables our robot to recognize named locations specified by a user in 84％ of cases.
Carl Case Bipin Suresh Adam Coates Andrew Y. Ng
Denotes equal contributions Department of Computer Science,Stanford University
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
3297-3303
2011-05-09（万方平台首次上网日期，不代表论文的发表时间）