April Tags and other passive fiducial markers are widely used to determine localization using a monocular camera. It utilizes specialized algorithms that detect markers to calculate their orientation and distance in three dimensional (3-D) space. The video and image processing steps performed to use these fiducial systems dominate the computation time of the algorithms. Low latency is a key component for the real-time application of these fiducial markers. The drawbacks of performing the video and image processing in software is the difficulty in performing the same operation in parallel effectively. Specialized hardware instantiations with the same algorithm scan efficiently parallelize them as well as operate on the image in a streaming fashion. Compared to graphics processing units (GPUs) that also perform well in the field, field programmable gate arrays (FPGAs) operate with less power, making them optimal with tight power constraints. This research describes such an optimization for the April Tag algorithm on an unmanned aerial vehicle with an embedded platform to perform real-time pose estimation, tracking, and localization in GPS-denied (global positioning system) environments at 30 frames per second (FPS) by converting the initial embedded C/C++ solution to a heterogeneous one through hardware acceleration. It compares the size, accuracy, and speed of the April Tag algorithm’s various implementations. The initial solution operated at around 2 FPS while the final solution, a novel heterogeneous algorithm on the Fusion 2 Zynq 7020 system on chip (SoC), operated at around 43 FPS using hardware acceleration. The research proposes a pipeline that breaks the algorithm into distinct steps where portions of it can be improved by utilizing algorithms optimized to run on a FPGA. Additional steps were made to further reduce the hardware algorithm’s resource utilization. Each step in the software was compared against its hardware counterpart using its utilization and timing as benchmarks.
Library of Congress Subject Headings
Drone aircraft--Control systems; Drone aircraft--Automatic control; Computer vision; Field programmable gate arrays
Computer Engineering (MS)
Department, Program, or Center
Computer Engineering (KGCOE)
Tola, Ethan, "Real-Time UAV Pose Estimation and Tracking Using FPGA Accelerated April Tag" (2021). Thesis. Rochester Institute of Technology. Accessed from
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