DockerSLAM

User-friendly way to run SLAM on Docker

This is a personal Dockerfile hub for SLAM algorithms.
Since SLAM consists of various modules, it is quite hard for a beginner to compile and run them from scratch.
This DockerSLAM repository is suitable for SLAM beginners. All you need is to install Docker beforehand!

Supported SLAM algorithm

For convenience, all SLAM algorithms are wrapped with ROS1 support.

LiDAR SLAM

Visual SLAM

How to use

0. Install Prerequisite

Docker

Please follow the official guidance link to install Docker.
Make sure to perform post-installation step, especially adding docker to your group.

newgroup docker

NVIDIA-Docker

In order to use --gpus all option when running a container, please install NVIDIA Container Toolkit properly.

1. Prepare Docker Image

First, check available algorithm (vins-mono, lio-sam, ... ) for Docker image.
You can either build it by yourself...

./build.sh <target_algorithm>

or just pull from dockerhub

docker pull hyeonjaegil/<target_algorithm>:latest

Check if image is ready with $ docker images command.

2. Run Container

./run <target_algorithm>

Customizing run.sh File

1. You can modify run.sh to use extra options, such as --volume option to mount your directories.
   For example, mount local ~/Download/Dataset folder into container /dataset folder.

# Inside run.sh file...

docker run --gpus all --rm -it --ipc=host --net=host --privileged \
    --env="DISPLAY" \
    --volume="/etc/localtime:/etc/localtime:ro" \
    --volume="$HOME/Downloads/Dataset:/dataset" \ # add this line.
    ${docker_image}

2. If you don't want to delete container when exiting, please remove --rm options.

Be aware that current run.sh file gives container the highest authority.

• It gives all available GPUs (--gpus all),
• gives maximum shared memory (--ipc host),
• offers the same network stack with host (--net host),
• and gives access to all the devices on the host (--privileged)
• AND container can connect from any host (xhost +)