This repository contains the source code for the paper "Image Generation for Efficient Neural Network Training in Autonomous Drone Racing" accepted at the WCCI 2020 congress.
Watch the video:
The nodes that can be ran onboard are located in the following packages:
controllers: which runs the camera driver, the Vicon bridge and the safety cageimg_center_alignment: which is the state machine and PID controllerperception: which runs the two CNNs (bounding box detection + distance estimation)
To build those, simply place them in your catkin workspace and run catkin build (or catkin_cmake if using the legacy build system). The Catkin
Command Line Tools build
system is recommended.
The three launch files are controllers control_inteldrone.launch,
perception detector.launch, and img_center_alignment controller.launch.
It is required to launch them in that specific order. Note that it is also
recommended to launch the perception node on a stationary computer, since the
inference is quite demanding.
In order to run the perception node, it is best to use the default Python2.7 provided with ROS Kinetic along with a virtualenv environment.
A virtual environment can be created with the given requirements.txt
dependency list, as such:
virtualenv myenv
source myenv/bin/activate
pip install -r requirements.txt
Use the hybrid-dataset-factory project, which can be found at https://github.com/M4gicT0/hybrid-dataset-factory. The mesh files used to generate the dataset in this paper can be found in the meshes/ folder.
The complete CNN model and code is provided in the perception package.
However, if one desires to study the code in detail, the repository is
available at
https://github.com/M4gicT0/mobilenet_v2_ssdlite_keras
in the custom_dataset branch.
For the distance estimation, only the saved model is given in the perception package. The source code is available at https://github.com/M4gicT0/GatePoseEstimator. It is still a work-in-progress but feel free to contribute.