Frappe: fast fiducial detection on low cost hardware

Square fiducial markers are widely used in robotics to easily obtain pose and other information about the world from camera images. Processing the images to extract the markers is usually performed centrally with standard libraries but the code is typically aimed at PC-level hardware. Platforms with constrained processing power have difficulty handling multiple camera streams at real-time refresh rates. We introduce the Frappe (Fiducial Recognition Accelerated with Parallel Processing Elements) algorithm for detecting and decoding the popular ArUco tags. Designed to be implemented on the low cost hardware of the Raspberry Pi Zero, we show tag detection and decoding on images of 640 × 480 resolution exceeding 60 Hz, five times faster than the standard ArUco library, while maintaining similar detection performance and using much less energy. Using Frappe, we demonstrate improved real-world performance on a visual navigation task with our DOTS robot.