Abstract
In this paper, we propose a vision-based feature tracker for the autonomous hovering of an unmanned aerial vehicle (UAV) and present an area-efficient hardware architecture for its integration into a flight control system-on-chip, which is essential for small UAVs. The proposed feature tracker is based on the Shi–Tomasi algorithm for feature detection and the pyramidal Lucas–Kanade (PLK) algorithm for feature tracking. By applying an efficient hardware structure that leverages the common computations between the Shi–Tomasi and PLK algorithms, the proposed feature tracker offers good tracking performance with fewer hardware resources than existing feature tracker implementations. To evaluate the tracking performance of the proposed feature tracker, we compared it with the GPS-based trajectories of a drone in various flight environments, such as lawn, asphalt, and sidewalk blocks. The proposed tracker exhibited an average accuracy of 0.039 in terms of normalized root-mean-square error (NRMSE). The proposed feature tracker was designed using the Verilog hardware description language and implemented on a field-programmable gate array (FPGA). The proposed feature tracker has 2744 slices, 25 DSPs, and 93 Kbit memory and can support the real-time processing at 417 FPS and an operating frequency of 130 MHz for 640 × 480 VGA images.
Highlights
Unmanned aerial vehicles (UAVs), often called quadcopters or drones, have been widely applied to execute missions in various fields, such as precision agriculture, security, and surveillance [1].One of the most popular and promising applications of unmanned aerial vehicle (UAV) is ground target surveillance that requires stable hovering of the UAV
We found that the degradation of the tracking performance when using down-sampling instead of decimation filtering is less than the root-mean-square error (RMSE) of one pixel
We proposed an area-efficient hardware architecture to perform feature tracking to support the autonomous hovering of UAVs
Summary
Unmanned aerial vehicles (UAVs), often called quadcopters or drones, have been widely applied to execute missions in various fields, such as precision agriculture, security, and surveillance [1]. Simultaneous localization and mapping (SLAM) techniques using depth sensors such as LiDAR or stereo-vision cameras build detailed maps of rooms and can be used for indoor hovering They require very complex computations and have high power consumption. The LK-OFE algorithm finds the flow vector of features by minimizing the sum of squared error between two images It exhibits excellent performance and has been used in many applications [13,14,15,16,17,18]. A small window is preferable to avoid smoothing out the details of the images, but a large window is required to handle large motion To overcome this problem, the pyramidal LK (PLK) algorithm was proposed in [19].
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