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https://github.com/shineyruan/cad2cav_onboard
On-board part of Project CAD2CAV: Computer Aided Design for Cooperative Autonomous Vehicles
https://github.com/shineyruan/cad2cav_onboard
autonomous-racing autonomous-vehicles cad2cav f1tenth object-detection ros
Last synced: 5 days ago
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On-board part of Project CAD2CAV: Computer Aided Design for Cooperative Autonomous Vehicles
- Host: GitHub
- URL: https://github.com/shineyruan/cad2cav_onboard
- Owner: shineyruan
- Created: 2021-06-12T06:35:01.000Z (over 3 years ago)
- Default Branch: master
- Last Pushed: 2021-11-21T05:19:10.000Z (almost 3 years ago)
- Last Synced: 2024-10-18T08:31:24.159Z (28 days ago)
- Topics: autonomous-racing, autonomous-vehicles, cad2cav, f1tenth, object-detection, ros
- Language: C
- Homepage: https://github.com/mlab-upenn/ISP2021-cad2cav
- Size: 28.1 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# cad2cav_onboard
Welcome to the on-board part of CAD2CAV project. For a detailed project documentation, see [the main repo.](https://github.com/mlab-upenn/ISP2021-cad2cav).
## System Requirements
- Nvidia Jetson Xavier NX Development Kit (hardware device)
- Linux Ubuntu 20.04 LTS/18.04 LTS
- ROS Noetic/Melodic
- GCC 7+/Clang 7+
## Software Requirements
- Boost 1.71
- [OpenCV 4.5.1](https://github.com/opencv/opencv/tree/4.5.1)
- [TensorRT 8.0.1](https://developer.nvidia.com/tensorrt-getting-started)
First run and install the following packages from Ubuntu repository:
```bash
sudo apt install libboost-all-dev
```
Then install OpenCV 4.5.1 on Xavier NX board by running
```bash
cd scripts
sudo ./Install_OpenCV-4-5-1.sh
```
***Note.* It might take ~2 hrs to fully install OpenCV 4.5.1 on the device.**
Then install other dependencies manually, by either building from source or following the official documentation.
## Installation
The entire process is ready for installation on an F1/10 Autonomous Racing Car, which uses an **Nvidia Jetson Xavier** Board. Users must also install the system-level drivers for F1/10 cars from [this repository.](https://github.com/f1tenth/f1tenth_system.git)
```bash
mkdir -p ~/catkin_ws/src
cd ~/catkin_ws
catkin_init_workspace
cd src
git clone https://github.com/shineyruan/cad2cav_onboard.git
git clone https://github.com/f1tenth/f1tenth_system.git
git clone --recursive https://github.com/shineyruan/cad2cav_common.git
cd ..
```
To build the project on the car (ROS Melodic), **you have to manually specify Python 3 path for cv_bridge building compliance.** Run:
```bash
catkin_make_isolated --use-ninja -DPYTHON_EXECUTABLE=/usr/bin/python3 -DPYTHON_INCLUDE_DIR=/usr/include/python3.6m -DPYTHON_LIBRARY=/usr/lib/aarch64-linux-gnu/libpython3.6m.so
source devel_isolated/setup.bash
```
### Optional Dependencies
For object detection with YOLO in darknet architecture, one could also experiment with [Shumin326/darknet_ros](https://github.com/Shumin326/darknet_ros). The package has been adapted from the upstream repo and is verified to run successfully on the F1Tenth car.
```bash
cd ~/catkin_ws/src
git clone --recursive https://github.com/Shumin326/darknet_ros
cd ..
```
## Usage
1. For object detection, run
```bash
rosrun object_detection object_detection_node --model=darknet
```
You can also specify a pre-recorded video to visualize by adding `--video=` as a command line argument.
## Acknowledgements
**The object detection** part of project uses a pre-trained model of Tiny-YOLO v4 directly from [the author's website.](https://github.com/AlexeyAB/darknet)