Indoor Furniture and Room Recognition for a Robot Using Internet-Derived Models and Object Context

Abstract

For robots to be able to fluidly collaborate with and keep company to humans in indoor spaces, they need to be able to perceive and understand such environments, including furniture and rooms. Towards that goal, we present a system for indoor furniture and room recognition for robots, which has two significant novelties: it utilizes internet-derived as well as self-captured models for training, and also uses object- and room-context information mined through the internet, in order to bootstrap and enhance its performance. Thus, the system also acts as an example of how autonomous robot entities can benefit from utilizing online information and services. Many interesting sub problems, including the peculiarities of utilizing such online sources, are discussed, followed by a real world empirical evaluation of the system, which shows highly promising results.