R7viz is a protocol that integrates front-end & back-end, which show autonomous driving data in real time.
Video (Testing rosbag) :
Video (Testing on Ipad) :
1. Development Environment
2. Key Features
3. Diagram
4. Installing Dependecy
5. Installing Package Scenario
6. Running Package Scenario
7. References
- OS : Ubuntu 18.04 LTS
- Processor : Intel(R) Core(TM) i7-8700K CPU @ 3.70GHz
- Mainboard : PRIME Z390-A
- RAM : 64GB
- GPU : GeForce GTX TITAN X
- Tool : Visual Studio Code
- Language : Javascript, CSS, HTML, ros-melodic
- Visualization of Camera Data
- Visualization of Lidar data
- Visualization of Velocity data
- Visualization of Acceleration Data
- Localization
- Direction of object
- Bounding box of object
- Data labeling of object
- HD Map
- Change View Mode
- Measurement of frame per second
- DGIST rosbag: a collection of sensor data obtained from DGIST self-driving vehicles
- rosbridge server: web servers accepting rostopic
- xviz converter: module that transforms the rostopic data in xviz format
- rosbridge-xviz-connector: XVIZ protocol data proxy server produced by the University of Toronto, Canada
- mapbox.com: Open Source Maps
- Map Source Selection: module that allows you to specify map box token and map style
- HD Map (GeoJson): DGIST's HD Map osm format files separated by lane and center line
- Web UI: Web-based UI customizing Uber's open source Streetscape.gl
- ros-bridge
$ sudo apt-get install ros-melodic-rosbridge-server
$ source /opt/ros/melodic/setup.bash
$ roslaunch rosbridge_server rosbridge_websocket.launch- graphic-driver
$ wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.1.243-1_amd64.deb
$ sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub
$ sudo dpkg -i cuda-repo-ubuntu1804_10.1.243-1_amd64.deb
$ sudo apt-get update
$ wget https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
$ sudo apt install ./nvidia-machine-learning-repo-ubuntu1804_1.0.0-1_amd64.deb
$ sudo apt-get update
$ sudo apt-get install —no-install-recommends nvidia-driver-450
$ reboot
$ nvidia-smi- nodejs, npm
$ sudo apt-get install build-essential libssl-dev
$ curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.33.11/install.sh | bash
$ source ~/.bashrc
$ nvm —version
$ nvm install 10.16
$ node --version
$ npm —version- yarn
$ curl -sS https://dl.yarnpkg.com/debian/pubkey.gpg | sudo apt-key add -
$ echo "deb https://dl.yarnpkg.com/debian/ stable main" | sudo tee /etc/apt/sources.list.d/yarn.list
$ sudo apt-get update && sudo apt-get install yarn- base64-to-uint8array
$ cd ~/
$ npm install base64-to-uint8array- sharp
$ cd ~/
$ npm install sharp- utm-latlng
$ cd ~/
$ npm install utm-latlng- math.gl
$ cd ~/
$ npm install math.gl- lodash
$ cd ~/
$ npm i -g npm
$ npm i --save lodash- turf
$ cd ~/
$ npm install @turf/turf- git clone
$ cd
$ https://github.com/AutonomousDriving-HMI/R7viz.git
$ cd ~/R7viz
$ cp -R morai_ws/ ~/- catkin_make & ros setting
$ cd ~/morai_ws
$ catkin_make
$ source devel/setup.bash
pwd를 통해 catkin_ws의 directory를 파악하여 bashrc daemon에 ros source를 추가한다.
$ sudo nano ~/.bashrc
마지막 줄로 이동한다.
source $현재 자신의 디렉토리$/devel/setup.bash를 추가한다.
해당 터미널을 종료하고 새로운 터미널에선 추가로 source devel/setup.bash를 안해도 된다.- XVIZ package
$ cd <R7viz directory>
$ cd rosbridge-xviz-connector
$ yarn