A pragmatic global vision system for educational robotics

This paper advocates the use of global vision as a tool for increasing the effectiveness of robotics education, and describes the design and functionality of advanced global vision systems used in our own programs. Our experiences with using global vision as a basis for teaching robotics and AI have led us to use this as a standard method for teaching undergraduates. Our recent vision systems (DORAEMON and ERGO) have consistently been improved to perform accurately and robustly over a wide range of applications. DORAEMON uses a sophisticated camera calibration method and colour model to remove the need for an overhead view of the world. ERGO minimized the use of colour information to provide more robust object recognition under varying lighting scenarios. Most recently, these video servers have been used by undergraduates to develop autonomous robots for a mixed virtual/physical world.