Small-scale unmanned aerial vehicles (UAVs) are an emerging research area and have been demonstrated in many applications today.
From the research perspective, we discovered many applications where UAVs can assist humans. Disaster response management, con- struction site monitoring and wide area surveillance are such arising applications where UAVs impose various benefits. However, single manually controlled UAVs face some limitations.
In this work we present a system built from multiple networked UAVs for monitoring a wide area scenario autonomously. Participating UAVs are equipped with various sensors and high resolution cameras. Due to the strong resource limitations such as energy supply, computation and communication, we implement an incremental approach for generating an orthographic mosaic from individual images of the surveillance area.
Captured images are pre-processed on-board and annotated with other sensor data. The data transfer is realized by a prioritized data transmission scheme to efficiently utilize the wireless network. The ultimate goal of our approach is to present the final overview image as fast as possible and to improve its quality over time. The mosaicking exploits position and orientation data of the UAV to compute rough image projections which are incrementally refined by scene structure analysis when more image data is available. We evaluate the benefits of incremental processing in the strongly resource limited UAV network and demonstrate the fast mosaic generation in our experiments with up to three concurrently flying UAVs. Our re- sults are compared to state-of-the-art mosaicking methods but show a unique performance in our dedicated application scenarios.