27 September 2023
Scaling up robot numbers in real-world environments requires both lowering the cost of robots and improving their ability to perceive and interact with the world. One approach uses cheap vision hardware and augments the environment with markers. Square fiducial markers consisting of a grid with a binary pattern 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.
In this work, EMERGE partners from the University of Bristol introduce an image processing algorithm called Frappe (Fiducial Recognition Accelerated with Parallel Processing Elements) for detecting and decoding the popular ArUco tags. Designed to be implemented on the low-cost hardware of the Raspberry Pi Zero, the authors show tag detection and decoding on images of 640 x 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, they demonstrate improved real-world performance on a visual navigation task with our DOTS robot.
As proof-of-concept, the authors implement Frappe on a swarm of DOTS robots designed for intralogistics applications. By re-engineering the visual navigation system of the DOTS, enabling higher detection framerates and resolutions than were previously possible, they demonstrate improved real-world performance on a visual navigation task
Source: Jones, S., Hauert, S. Frappe: fast fiducial detection on low cost hardware. J Real-Time Image Proc 20, 119 (2023). DOI: 10.1007/s11554-023-01373-w.
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