Real-Time ORB Accelerator with ROS Integration for Embedded FPGA SoCs

As computer vision continues to expand across various application domains - including localisation, mapping, object recognition, and 3D reconstruction - feature extraction methods such as Oriented FAST and Rotated BRIEF (ORB) gained widespread adoption due to their rotation and scale invariance. However, existing efforts to accelerate these techniques through hardware implementations faced challenges related to high resource and power consumption demands, limiting their feasibility for low-power embedded devices. Accordingly, this paper proposes a new scalable and efficient ORB accelerator, designed for low-power resource-constrained environments. It introduces a novel resource-efficient architecture that exploits quantisation of the feature orientation angle into discrete rotation sectors. A complete ROS node based on the proposed ORB accelerator is also deployed, providing seamless integration with other computer vision-enabled systems. When compared to other state-of-the-art solutions, the proposed system, implemented on an embedded System-on-Chip (SoC) with a low-cost FPGA, offers between 6.7x and 16.2x energy efficiency improvements, while requiring fewer hardware resources.