Real-Time ORB Accelerator for Embedded FPGA-Based SoCs With ROS Integration

As computer vision continues to expand across various application domains—including localization, mapping, object recognition, and 3-D reconstruction—feature extraction methods, such as oriented FAST and rotated BRIEF (ORB), have gained widespread adoption due to their rotation and scale invariance. However, existing efforts to accelerate these techniques through hardware implementations faced different challenges related to resource and power consumption demands, limiting their feasibility for low-power embedded devices. Accordingly, this article proposes a new scalable and efficient ORB accelerator, designed for low-power and resource-constrained environments. It introduces a novel and efficient architecture that exploits quantization of the feature orientation angle into discrete rotation sectors. A complete robot operating system (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 field-programmable gate array (FPGA), offers energy efficiency improvements between <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$6.7\times $</tex-math> </inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$16.2\times $</tex-math> </inline-formula>, while requiring fewer hardware resources.