This repo contains source code for vision-based navigation in ROS. We combine deep learning and traditional computer vision methods along with ArUco markers for relative positioning between the camera and the marker. Both monocular and stereo vision are used for comparison. It is tested with Ubuntu 18.04 LTS and ROS melodic on an Nvidia Jetson Xavier. The figure below shows the high-level architecture with focus on stereo vision.
Please include the following reference if you use the toolbox: Ø. Volden (2020)."Vision-Based Navigation in ROS". URL: https://github.com/oysteinvolden/vision-based-navigation
Bibtex entry: @misc{VoldenVISIONROS2020, title = {Vision-{B}ased {N}avigation in {ROS}}, author = {{{\O}}. Volden}, howpublished = {\url{https://github.com/oysteinvolden/vision-based-navigation}}, year = {2020} }
CUDA
Cuda is preinstalled on Nvidia Jetson Xavier.
ROS
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If you use a standard PC with x86 architecture, follow the instructions here: http://wiki.ros.org/melodic/Installation/Ubuntu. Full-desktop version is recommended if disk-space is not critical.
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For arm-based Nvidia Jetson Xavier:
git clone https://github.com/jetsonhacks/installROSXavier cd installROSXavier ./installROS.sh -p ros-melodic-desktop-full
This repo also provide a quick solution to setup a catkin workspace by running the command:
./setupCatkinWorkspace.sh
OpenCV
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For standard laptops, installation instructions from here is recommended: https://www.pyimagesearch.com/2018/05/28/ubuntu-18-04-how-to-install-opencv/.
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For Jetson Xavier:
git clone https://github.com/AastaNV/JEP cd script ./install_opencv4.1.1_Jetson.sh
The darknet_ros package is used for the left camera, while the darknet_ros_2 package is used for the right camera.
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First clone the repo get access to both.
git clone --recursive https://github.com/oysteinvolden/vision-based-navigation.git -
Create a catkin workspace.
mkdir -p darknet_ws/src cd darknet_ws/src -
copy recursively the darknet_ros, darknet_ros_msgs and darknet folders from the cloned repository to the catkin environment (/darknet_ws/src) and include ROS package for bridging opencv and ROS (vision_opencv).
git clone https://github.com/ros-perception/vision_opencv.git cd .. catkin_make -DCMAKE_BUILD_TYPE=Release
Repeat 2-3 for a second catkin workspace (e.g. /darknet_ws2) suitable for the right camera. Remember to copy recursively the darknet_ros_2 folder to the catkin environment (/darknet_ws2/src) instead of the darknet_ros folder.
Configuration
It is assumed that the trained weights and configuration (cfg) file is available for YoloV3 (darknet).
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darknet folder:
- Adjust Makefile such that GPU, Opencv, CuDNN, CuDNN_HALF and OpenMP is enabled (uncommented)
- Also enable jetson Xavier Arch and disable all others (in Makefile). Then make locally in the darknet folder.
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darknet_ros folder:
- in yolo_network_config:
- put trained weights and cfg file (under subfolders weights and cfg, respectively)
- in darknet_ros/config:
- yolov3-spp.yaml: Ensure the content match what is used in the yolo_network_config
- ros.yaml: The topic under "camera_reading" should match the ros topic name sent from the camera driver, e.g. /camera_array/cam0/image_raw or /camera_array/cam1/image_raw. Just use one camera topic name per ROS node for subscription
- in darknet_ros/launch:
- darknet_ros.launch and yolov3-spp.launch: Check that they include correct weights/cfg and yaml file (yolov3-spp.yaml)
- in yolo_network_config:
NB! Calibration parameters and camera resolution is defined in the source code for specific purposes. Marker specific configuration is also specified in the source code.
We use a ROS compatible camera driver. By this, the camera driver and the object detection pipeline can interchange data via ROS topics. Follow the instructions in this github repository to create a catkin workspace. We use hardware triggering (GPIO connector) for stereo setup as described under "Multicamera Master-Slave Setup" in the github repo. When GPIO cables are connected correctly, do the following:
- In params:
- stereo_camera_example.yaml: Change camera ids to serial numbers of the actual cameras. Make sure left camera is master camera (primary).
- in launch:
- acquisition.launch: change from test_params.yaml to stereo_camera_example.yaml (line 22).
We use a ROS compatible LiDAR driver for verification of camera measurements, i.e. not a part of the core functionality. Follow the instructions in the github repository to interface and create a catkin workspace.
This section shows the specific hardware in use.
- 2 x Camera: FLIR Blackfly S GigE (BFS-PGE-13Y3C-C).
- 2 x Lens: Edmund Optics.
- GPIO connector: Hirose HR10.
- Lidar: Ouster Os1.
- PC: Nvidia Jetson Xavier.
- DC/DC converters.
- PoE adapters.
- Switch / ethernet cables.
The figure below shows the power and ethernet interface between the hardware components and the guidance, navigation, and conrol (GNC)computer for the USV.
Datasets, model parameters and test scenarios can be found in the following link: https://drive.google.com/drive/folders/1jc0gy81dSRRfRxAGuXiN1P2s_waCPXsG?usp=sharing
To launch the driver, open a terminal and type:
cd ~/spinnaker_ws
source devel/setup.bash
roslaunch spinnaker_sdk_camera_driver acquisition.launch
To launch the driver, open a terminal and type:
cd ~/myworkspace
source devel/setup.bash
roslaunch ouster_ros os1.launch os1_hostname:=os1-991837000010.local lidar_mode:=2048x10 os1_udp_dest:=192.168.11.219
- <os1_hostname> can be the hostname (os1-991xxxxxxxxx) or IP of the OS1 (serial number)
- <lidar_mode> is one of 512x10, 512x20, 1024x10, 1024x20, or 2048x10 (optical resolution)
- <udp_data_dest_ip> is the IP to which the sensor should send data (make sure it is on the 192.168.x.x subnet)
Open a terminal and type the following:
cd ~/darknet_ws
source devel/setup.bash
roslaunch darknet_ros yolov3-spp.launch
Go into a second terminal and type the following:
cd ~/darknet_ws2
source devel/setup.bash
roslaunch darknet_ros yolov3-spp.launch
Hence, you have one ROS node per camera and they run simultaneously.
Images
/camera_array/cam0/image_raw([sensor_msgs/Image])/camera_array/cam1/image_raw([sensor_msgs/Image])
Original image data from a pair of FLIR blackfly S cameras.
Point cloud
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/os1_cloud_node/points([sensor_msgs/Pointcloud2])Original point cloud data from an Ouster Os1 lidar.
Author: Oystein Volden, [email protected]
Credit: The pipeline is further extended and developed for 3D localization tasks based on relevant object detection frameworks such as YOLO ROS: Real-Time Object Detection for ROS by Marko Bjelonic and YOLO ROS: Real-Time Object Detection for ROS by Pushkal Katara.