Mobile robot navigation using binocular stereo vision - Gueta, Lounell B. (2005)
An Automated Guided Vehicle (AGV), mainly used in delivering stocks and products, is a byword in industries and warehouses. It is the result of early research works in mobile robotics involving remote steering of a robotic platform. While AGV's have been widely accepted and used, an immense part of the work is focused on developing inventive ways of providing a human operator with the information and interface as an aid for control. With the advances in sensor and computing technology, the level of autonomy of remote agents or mobile robots is increased. With less human intervention, the agents can reflexively interact with its environment. This is achieved by the integration of active sensing elements, such as infrared sensors, sonar devices, and laser range finders, to the platform. In this study, a mobile robot is developed that utilizes passive sensing elements which are two black-and-white Charge Coupled Device (CCD) cameras. In contrast to active sensing elements which send signals in the form of modulated infrared light or sound waves, the stereo cameras simply capture images to discern objects in the robot's environment. This research intially involves the integration of the binocular stereo cameras, a low-cost TV tuner as the frame grabber, an AMD Athlon XP 2400+ processor running in Redhat Linux 7.2 OS, a wireless network card for remote control during the early stage of the research, and all the All-Terrain Mobile Robot (ATRV) platform by IS Robotics, An image processing module is then developed using area-based stereo matching. Finally, the robot navigation is implemented based on Virtual Field Histogram (VFH) algorithm. In the tests conducted, the stereo matching algorithm utilizes Sum-of-Absolute-Difference (SAD) AND Sum-of-Squared-Difference (SSD) stereo matching metrics, an averaging filters as aggregation method, and Winner-Take-All (WTA) optimization. Based on the results, the proposed weighted-mean disparity (WMD) conversion outperforms the commonly-used maximum disparity conversion method. The stereo matching technique employing WMD is compared to stereo matching techniques employing Moravee texture test and bi-directional stereo matching as disparity refinement methods.