The Minnesota robotic visual tracker: a flexible testbed for vision-guided robotic research

Most early research in robotic visual tracking, especially prior to 1990, separated the vision processing and robot control aspects of the system. Attempts to solve the problem close the control loop by incorporating the output of the vision processing as an input to the control subsystem. The controlled active vision framework describes one such approach wherein dynamic target, camera, and environmental factors are incorporated via adaptive controllers that utilize the sum-of-squared differences (SSD) optical flow measurements as an input to the control loop. This paper describes work at the University of Minnesota's Artificial Intelligence, Robotics, and Vision Laboratory in developing the Minnesota Robotic Visual Tracker (MRVT), a controlled active vision robotic testbed. In addition, enhancements to the basic SSD algorithm are presented that produce order-of-magnitude improvements over previously reported results.<>