nanoflownet-cnns

This repository contains the code used to create and train NanoFlowNet, and includes pre-trained networks.

Please refer to the main repository for the AI-deck implementation and the Crazyflie obstacle avoidance application

Install

The easiest way to set up is to install all requirements into a docker environment:

docker run -v <path to FlyingChairs2>:/workspace/FlyingChairs2 -v <path to flow datasets dir>:/workspace/flowData -v <path to this repo>:/workspace/nanoflownet --gpus all -it tensorflow/tensorflow:2.8.0-gpu

Inside the created docker container:

pip install opencv-python-headless==4.5.5.64

pip install tensorflow_model_optimization==0.7.2

pip install tqdm==4.64.0

pip install tensorflow_addons==0.16.1

pip install wandb==0.12.14

pip install --extra-index-url https://developer.download.nvidia.com/compute/redist --upgrade nvidia-dali-tf-plugin-cuda110==1.12.0

in another terminal find the docker container id with docker ps -l

and commit the changes

docker commit [container_id] nanoflownet

This concludes the set-up. The correct container can be now opened (without re-installing the pip requirements) by replacing tensorflow/tensorflow:2.8.0-gpu with nanoflownet:

docker run -v <path to FlyingChairs2>:/workspace/FlyingChairs2 -v <path to flow datasets dir>:/workspace/flowData -v <path to this repo>:/workspace/nanoflownet --gpus all -it nanoflownet

Publication:

arXiv preprint

IEEE-ICRA 2023 paper

Please cite us as follows:

@inproceedings{bouwmeester2023nanoflownet,
  title={Nanoflownet: Real-time dense optical flow on a nano quadcopter},
  author={Bouwmeester, Rik J and Paredes-Vall{\'e}s, Federico and De Croon, Guido CHE},
  booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)},
  pages={1996--2003},
  year={2023},
  organization={IEEE}
}