Area-Efficient Vision-Based Feature Tracker for Autonomous Hovering of Unmanned Aerial Vehicle

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 x 480 VGA images.