NanoFlowNet

NanoFlowNet: Real-time optical flow estimation on a nano quadcopter

Bouwmeester, Rik (TU Delft Aerospace Engineering)

de Croon, G.C.H.E. (mentor)
Paredes-Vallés, Federico (mentor)

Delft University of Technology

Aerospace Engineering

2022-10-13

Nano quadcopters are small, agile, and cheap platforms well suited for deployment in narrow, cluttered environments. Due to their limited payload, nano quadcopters are highly constrained in processing power, rendering conventional vision-based methods for autonomous navigation incompatible. Recent machine learning developments promise high-performance perception at low latency, while novel ultra-low power microcontrollers augment the visual processing power of nano quadcopters. In this work, we present NanoFlowNet, an optical flow CNN that, based on the semantic segmentation architecture STDC-Seg, achieves real-time dense optical flow estimation on edge hardware. We use motion boundary ground truth to guide the learning of optical flow, improving performance with zero impact on latency. Validation on MPI-Sintel shows the high performance of the proposed method given its constrained architecture. We implement the CNN on the ultra-low power GAP8 microcontroller and demonstrate it in an obstacle avoidance application on a 34 g Bitcraze Crazyflie nano quadcopter.

MAV
CNN
edge AI
optical flow

http://resolver.tudelft.nl/uuid:574db806-6096-4600-9926-3d737d1ee7da

Student theses

master thesis

© 2022 Rik Bouwmeester