Improving Self-Localization Efficiency in a Small Mobile Robot by Using a Hybrid Field of View Vision System
Journal of Automation, Mobile Robotics & Intelligent Systems
VOLUME 9, N° 4 2015 28 gorithms require data from highly precise sensors, such as laser scanners  , or have high computing power demands, if less precise data (e.g. from passive cameras) are used  . Thus, the SLAM approach is rather unsuitable for small mobile robots, such like our SanBot  , which have quite limited resources with respect to on-board sensing, computing power, and communication bandwidth. Thus, for such a robot an approach to self-localization that does not need to
... struct a map of the environment, or uses a simple and easy to survey representation of the known area is required. Moreover, the self-localization system should use data from compact and low-cost sensors. In the context of navigation CCD/CMOS cameras are the most compact and low-cost sensors for mobile robots  . However, most of the passive vision-based localization methods fail under natural environmental conditions, due to occlusions, shadows, changing illumination, etc. Therefore, in practical applications of mobile robots artificial landmarks are commonly employed. They are objects purposefully placed in the environment, such as visual patterns or reflecting tapes. Landmarks enhance the efficiency and robustness of vision-based self-localization  . It was also demonstrated that simple artificial landmarks are a valuable extension to visual SLAM  . An obvious disadvantage is that the environment has to be engineered. This problem can be alleviated by using simple, cheap, expendable and unobtrusive markers, which can be easily attached to walls and various objects. In this research we employ simple landmarks printed in black and white that are based on the matrix QR (Quick Response) codes commonly used to recognize packages and other goods. In our recent work  we evaluated the QR code landmarks as self-localization aids in two very different configurations of the camera-based perception system: an overhead camera that observed a landmark attached on top of a mobile robot, and a frontview camera attached to a robot, which observed landmarks freely placed in the environment. Both solutions enable to localize the robot in real-time with a sufficient accuracy, but both have important practical drawbacks. The overhead camera provides inexpensive means to localize a group of few small mobile robots in a desktop application, but cannot be easily scaled up for larger mobile robots operating in a real environment. The front-view camera with on-board image processing is a self-contained solution for selflocalization, which enables the robot to work autonomously, making it independent from possible com- Abstract: In this article a self-localization system for small mobile robots based on inexpensive cameras and unobtrusive, passive landmarks is presented and evaluated. The main contribution is the experimental evaluation of the hybrid field of view vision system for self-localization with artificial landmarks. The hybrid vision system consists of an omnidirectional, upward-looking camera with a mirror, and a typical, front-view camera. This configuration is inspired by the peripheral and foveal vision co-operation in animals. We demonstrate that the omnidirectional camera enables the robot to detect quickly landmark candidates and to track the already known landmarks in the environment. The front-view camera guided by the omnidirectional information enables precise measurements of the landmark position over extended distances. The passive landmarks are based on QR codes, which makes possible to easily include in the landmark pattern additional information relevant for navigation. We present evaluation of the positioning accuracy of the system mounted on a SanBot Mk II mobile robot. The experimental results demonstrate that the hybrid field of view vision system and the QR code landmarks enable the small mobile robot to navigate safely along extended paths in a typical home environment.