# Parallelizing Optical Flow Estimation on an Ultra-Low Power RISC-V Cluster for Nano-UAV Navigation

arXiv: https://arxiv.org/abs/2305.13055 · HTML: https://mlsanity.com/paper/2305.13055/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 22 references that are in the map.
- First posted on arXiv 22 May 2023.

## Builds on (most cited first, 8 of 8)

- [FlowNet: Learning Optical Flow with Convolutional Networks](https://mlsanity.com/paper/1504.06852/) (2015)
- [Selective Sensor Fusion for Neural Visual-Inertial Odometry](https://mlsanity.com/paper/1903.01534/) (2019)
- [FastFlowNet: A Lightweight Network for Fast Optical Flow Estimation](https://mlsanity.com/paper/2103.04524/) (2021)
- [A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones](https://mlsanity.com/paper/1805.01831/) (2018)
- [Vega: A 10-Core SoC for IoT End-Nodes with DNN Acceleration and Cognitive Wake-Up From MRAM-Based State-Retentive Sleep Mode](https://mlsanity.com/paper/2110.09101/) (2021)
- [Vertical Landing for Micro Air Vehicles using Event-Based Optical Flow](https://mlsanity.com/paper/1702.00061/) (2017)
- [Block-Matching Optical Flow for Dynamic Vision Sensor- Algorithm and FPGA Implementation](https://mlsanity.com/paper/1706.05415/) (2017)
- [Towards Robust Monocular Visual Odometry for Flying Robots on Planetary Missions](https://mlsanity.com/paper/2109.05509/) (2021)

## Built on by (most cited first, 3 of 3)

- [LEVIO: Lightweight Embedded Visual Inertial Odometry for Resource-Constrained Devices](https://mlsanity.com/paper/2602.03294/) (2026)
- [Planar Velocity Estimation for Fast-Moving Mobile Robots Using Event-Based Optical Flow](https://mlsanity.com/paper/2505.11116/) (2025)
- [Efficient and Accurate Downfacing Visual Inertial Odometry](https://mlsanity.com/paper/2509.10021/) (2025)

## Abstract (arXiv)

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.