Optical flow estimation is crucial for autonomous navigation and localization of unmanned aerial vehicles (UAV). On micro and nano UAVs, real-time calculation of the optical flow is run on low power and resource-constrained microcontroller units (MCUs). Thus, lightweight algorithms for optical flow have been proposed targeting real-time execution on traditional single-core MCUs. This paper introduces an efficient parallelization strategy for optical flow computation targeting new-generation multicore low power RISC-V based microcontroller units. Our approach enables higher frame rates at lower clock speeds. It has been implemented and evaluated on the eight-core cluster of a commercial octa-core MCU (GAP8) reaching a parallelization speedup factor of 7.21 allowing for a frame rate of 500 frames per second when running on a 50 MHz clock frequency. The proposed parallel algorithm significantly boosts the camera frame rate on micro unmanned aerial vehicles, which enables higher flight speeds: the maximum flight speed can be doubled, while using less than a third of the clock frequency of previous single-core implementations.

翻译：光流估计对于无人机的自主导航与定位至关重要。在微型及纳米级无人机上，光流的实时计算需要在低功耗且资源受限的微控制器单元（MCU）上完成。因此，针对传统单核MCU的实时执行需求，已提出了多种轻量级光流算法。本文提出了一种高效的并行化策略，旨在基于新一代多核低功耗RISC-V微控制器单元实现光流计算。我们的方法能够在较低时钟频率下获得更高的帧率。该策略已在商用八核MCU（GAP8）的八核集群上实现并评估，并行加速比达到7.21，在50 MHz时钟频率下可实现每秒500帧的帧率。所提出的并行算法显著提升了微型无人机的相机帧率，从而支持更高的飞行速度：相较于以往的单核实现方案，最大飞行速度可提升一倍，而时钟频率仅需不到其三分之一。