Common field-of-view of cameras in robotic swarms

Cooperative swarms of robots with cameras can provide stereo and multi-view vision. They are robust against failures and introduce diversity in the case of poor conditions. Cameras are used both for data acquisition and navigation. Additional sensors also play a role in the SLAM tasks (Simultaneously Localization and Mapping). The independent mobility of the robots/cameras leads to a situation in which the common field-of-view (FOV) of cameras changes continuously. The present paper addresses the task of acquiring and tracking the cameras FOVs. State-of-the-art FOV characterization techniques count feature points or do image segmentation. These methods are often not accurate or complex. We propose an adaptive common FOV detection method based on fuzzy plane clustering. The performance of the method is shown to be invariant under baseline scaling. An autonomous grouping algorithm is further proposed with respect to both distance of robots and overlapping FOV of cameras.