Autonomous visual navigation components for drones and ground vehicles using deep learning. Refer to wiki for more information on how to get started.
This project contains deep neural networks, computer vision and control code, hardware instructions and other artifacts that allow users to build a drone or a ground vehicle which can autonomously navigate through highly unstructured environments like forest trails, sidewalks, etc. Our TrailNet DNN for visual navigation is running on NVIDIA's Jetson embedded platform. Our arXiv paper describes TrailNet and other runtime modules in detail.
The project's deep neural networks (DNNs) 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 project also contains Stereo DNN models and runtime which allow to estimate depth from stereo camera on NVIDIA platforms.
2020-02-03: Alternative implementations. redtail is no longer being developed, but fortunately our community stepped in and continued developing the project. We thank our users for the interest in redtail, questions and feedback!
Some alternative implementations are listed below.
2018-10-10: Stereo DNN ROS node and fixes.
2018-09-19: Updates to Stereo DNN.
ResNet18 2Dmodel, resulting in 2x performance increase (20fps on Jetson TX2).
ResNet18 2Dmodel to reduce model loading time from minutes to less than a second.
2018-06-04: CVPR 2018 workshop. Fast version of Stereo DNN.
2018-03-22: redtail 2.0.
2018-02-15: added support for the TBS Discovery platform.
2017-10-12: added full simulation Docker image, experimental support for APM Rover and support for MAVROS v0.21+.
2017-09-07: NVIDIA Redtail project is released as an open source project.
Redtail's AI modules allow building autonomous drones and mobile robots based on Deep Learning and NVIDIA Jetson TX1 and TX2 embedded systems. Source code, pre-trained models as well as detailed build and test instructions are released on GitHub.
TensorRT 2.1 provides significant improvements in DNN inference performance as well as new features and bug fixes. This is a breaking change which requires re-flashing Jetson with JetPack 3.1.