Eye-to-Hand Robotic Visual Tracking Based on Template Matching on FPGAs

Abstract

This paper presents a delta robotic visual-servoing tracking method using zero-mean normalized cross-correlation (ZNCC)-based grayscale template matching hardware core on field-programmable gate arrays (FPGAs). The concurrent FPGA-based ZNCC hardware core with cascading multiplication-accumulate (MAC) circuits is designed, which can largely reduce FPGA hardware resource consumptions. A compact optical imaging system with a front 45°-slant mirror and an optical filter film are proposed, which can efficiently filter out the background cluttered artifacts. The trajectory visual-servoing tracking and dynamic tracking experiments based on our built-up delta robotic visual tracking platform are implemented. The experimental results indicate that the presented FPGA-based embedded robotic visual tracking method can efficiently improve an object trajectory tracking performance.