CoSy Book Videos
A Discriminative Approach to Robust Visual Place Recognition
An important competence for a mobile robot system is the ability to localize and perform context interpretation. This is required to perform basic navigation and to facilitate local specific services. Usually localization is performed based on a purely geometric model. Through use of vision and place recognition a number of opportunities open up in terms of flexibility and association of semantics to the model. To achieve this the video presents an appearance based method for place recognition. The method is based on a large margin classifier in combination with a rich global image descriptor. The method is robust to variations in illumination and minor scene changes. The method is evaluated across several different cameras, changes in time-of-day and weather conditions.
Multi-modal Semantic Labeling of Space
The problem of semantic labeling can be described as assigning meaningful semantic descriptions (e.g. "corridor" or "kitchen") to areas in the environment. Typically, semantic labeling is used as a way of augmenting the internal space representation of a robot with additional, more abstract information. This can be used by the robotic agent to enhance communication with a human user or to reason about space. The video presents a real-time experiment performed at the University of Birmingham, UK. In the experiment, the robot builds a multi-layered spatial representation with semantic place information based on multi-modal sensory input (vision and laser range data).
PlayMate Year 3 system
The PlayMate can moce objects around on request and can cope if things don't turn out as planned.
PlayMate Year 4 system
The PlayMate can watch a human playing simple games and answer questions about what it sees.