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This project contains instructions, code and other artifacts that allow users to build a drone which can autonomously navigate through highly unstructured environments like forest trails. The drone uses deep learning-based AI running on an NVIDIA Jetson embedded platform. The code implements ideas discussed in the arXiv paper, see references section.
The project has two major parts, modeling and platform implementation.
The project's AI that enables autonomous navigation is based on a deep neural network (DNN) which can be trained from scratch using publicly available data. A few pre-trained DNNs are also available as a part of this project. In case you want to train TrailNet DNN from scratch, follow the steps on this page.
The following drone platforms are currently supported:
In general, any drone platform that uses the Pixhawk flight controller should work too.
There is also an experimental support for APM Rover:
Building a complete autonomous drone platform requires proper hardware and software configuration.
The NVIDIA Jetson platform is used to run most of the components, such as DNN inference, the controller, and video streaming. The Jetson setup guide describes steps to install all required software and dependencies.
Depending on the drone platform some additional steps might be required. Follow the steps in the documentation for your particular platform:
A laptop is a convenient way to run GCS software like QGroundControl as well to control the drone using an NVIDIA Shield or an XBox controller. Follow these steps to setup GCS machine.
It is usually a good idea to test your code in a simulator. Follow these steps to run simulations using Gazebo.
Once the hardware and software setup steps are complete, it's time to take off! Follow these steps to fly the drone.
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