FPGA-aided MAV vision-based estimation
Author(s)Giraldez, Dember Alexander
Field-Programmable-Gate-Array-aided Micro Air Vehicle vision-based estimation.
Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.
Nicholas Roy and Richard Madison.
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The process of estimating motion trajectory through an unknown environment from a monocular image sequence is one of the main challenges in Micro Air Vehicle (MAV) navigation. Today MAVs are becoming more and more prevalent in both civilian and military operations. However, with their reduction in size compared to traditional Unmanned Aircraft Vehicles (UAVs), the computational power and payload that can be carried onboard is limited. While there is ample research in motion estimation for systems that are deployed on the ground, have various sensors, as well as multiple cameras, a current challenge consists of deploying minimalistic systems suited specifically for MAVs. This thesis presents a novel approach for six-degrees of freedom motion estimation using a monocular camera containing a Field-Programmable-Gate-Array (FPGA). Most implementations using a monocular camera onboard MAVs stream images to a ground station for processing. However, an FPGA can be programmed for feature extraction, so instead of sending raw images, information is encoded by the FPGA and only frame information, feature locations, and descriptors are transmitted. This onboard precomputation greatly reduces bandwidth usage and ground station processing. The objectives of this research are (1) to show how the raw computing power of an FPGA can be exploited in this application and (2) to evaluate the performance of such a system against a traditional monocular camera implementation. The underlying motivation is to bring MAV systems closer to complete autonomy, meaning all the computation needed for estimation and navigation is carried out autonomously and onboard.
Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.Cataloged from PDF version of thesis.Includes bibliographical references (p. 79-81).
DepartmentMassachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science.; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology
Electrical Engineering and Computer Science.