Vision-Based Egomotion Estimation on FPGA for Unmanned Aerial Vehicle Navigation

The use of unmanned aerial vehicles (UAVs) in commercial and warfare activities has intensified over the last decade. One of the main challenges is to enable UAVs to become as autonomous as possible. A vital component toward this direction is the robust and accurate estimation of the egomotion of the UAV. Egomotion estimation can be enhanced by equipping the UAV with a video camera, which enables a vision-based egomotion estimation. However, the high computational requirements of vision-based egomotion algorithms, combined with the real-time performance and low power consumption requirements that are related to such an application, cannot be met by general-purpose processing units. This paper presents a system architecture that employs a field-programmable gate array as the main processing platform connected to a low-power CPU that targets the problem of vision-based egomotion estimation in a UAV. The performance evaluation of the proposed system, using real data captured by a UAV's on-board camera, demonstrates the ability of the system to render accurate estimation of the egomotion parameters, meeting at the same time the real-time requirements imposed by the application.