Vision Pipeline

The ros_vision_pipeline is the docker container for the vision_pipeline.

Installation

Folder structure:

ros_vision_pipeline/
vision_files/
├─ datasets/
├─ vision_pipeline/

  1. Clone ros_vision_pipeline:

git clone git@github.com:ReconCycle/ros_vision_pipeline.git

  1. We pull all the submodules that are used in the catkin_ws. This includes the context action framework.

git submodule update --init --recursive

  1. Clone vision_pipeline and additional software:

git clone git@github.com:ReconCycle/vision_pipeline.git
git clone git@github.com:ReconCycle/device_reid.git
git clone git@github.com:ReconCycle/superglue_training.git

  1. In the docker-compose.yml file, the volumes should be set correctly.

volumes:
    - $HOME/path/to/vision_pipeline:/home/docker/vision_pipeline
    - $HOME/path/to/device_reid:/home/docker/device_reid
    - $HOME/path/to/superglue_training:/home/docker/superglue_training

  1. Copy the directory from the Nextcloud Reconcycle repository git-data/vision-pipeline/data to the vision-pipeline/data_limited folder.

  2. cp config.example.yaml config.yaml

Running The Pipeline

Run:

$ cd ros_vision_pipeline
$ docker-compose up -d

or

$ docker exec -it ros_vision_pipeline bash

Prerequisites

You need to install the Nvidia graphics drivers and the CUDA toolkit. The Nvidia drivers are also bundled with CUDA, but I had trouble installing it this way.

Installing Nvidia graphics drivers

Prequisites:

sudo apt install build-essential libglvnd-dev pkg-config

Now download here the nvidia drivers and run as root to install. The Nvidia drivers require gcc-9 which is what ubuntu ships with by default.

Installing CUDA toolkit

Install CUDA 11.3 on your host system. Go to Cuda Toolkit Archive then click on CUDA Toolkit 11.3. Select your operating system and download the runfile. Run the runfile using sudo.

  • You may need gcc version 8 to run CUDA 11.3. If so, run: sudo apt install gcc-8 g++-8. Guide here on how to switch gcc versions.

There is an installation guide for CUDA from nvidia here. You can have a look at it for reference.

Docker running as root

First you need to install nvidia-container-runtime, instructions here and run:

sudo apt-get install nvidia-container-runtime

Edit /etc/docker/daemon.json to contain:

{
    "default-runtime":"nvidia",
    "runtimes": {
        "nvidia": {
            "path": "nvidia-container-runtime",
            "runtimeArgs": []
        }
    }
}

Then: sudo systemctl daemon-reload

Check if runtime is added sucessfully: docker info|grep -i runtime

Docker running rootless (probably not what you want to do)

Do the same except put the file here: ~/.config/docker/daemon.json.

Then: systemctl --user daemon-reload

Check if runtime is added sucessfully: docker info|grep -i runtime

ONLY INSTALL DOCKER AFTER DOING THESE STEPS IF YOU ARE RUNNING ROOTLESS!

Running Docker container

Make sure you have everything set up from the previous sections. Install docker-compose.

Edit the docker-compose.yml file and remove the ROS master and Rviz if you already have these running elsewhere. In principle the docker-compose.yml file in its current state will provide you with a ROS master, and Rviz that can be accessed via the browser through the novnc container.

The container is running in host mode because this is the easiest way to give it access to the Basler camera. The ROS_IP needs to be set correctly. Do this by running $ hostname -I on the host and setting the ROS_IP to this IP (take the first one if it gives multiple IP addresses).