Parallelizing Optical Flow Estimation on an Ultra-Low Power RISC-V
  Cluster for Nano-UAV Navigation
Jonas Kühne Michele Magno Luca Benini
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.