Cartographer - Ros - Docker¶

Abstract

We provide a dockerized cartographer/ros stack for running a SLAM pipeline.

Use roverd rosbag to convert rover data to a rosbag.
Run cartographer using DATASET=... HEADLESS=true make lidar.

Setup¶

Ensure docker is installed:

sudo snap install docker
sudo apt-get install docker.io
sudo systemctl enable docker
sudo systemctl start docker

Install the Nvidia Container Toolkit:

distribution=$(. /etc/os-release;echo $ID$VERSION_ID)  

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

sudo apt-get update
sudo apt-get install -y nvidia-docker2  
sudo systemctl restart docker

Test the install (may require a reboot, especially if nvidia-smi returns an error):
```
sudo docker run --rm --gpus all nvidia/cuda:11.6.2-base-ubuntu20.04 nvidia-smi
```
Unable to load libnvidia-ml

A common error is a failure from the nvidia-container-cli noting that it was unable to load libnvidia-ml because of no such file or directory. This is caused by NVIDIA hating you, and the best fix seems to be to reinstall docker.io (i.e. the docker engine). If that does not work, reinstall everything else as well, which will typically resolve this issue.

(Tianshu, 06/2024) - I suspect this may be caused by some kind of time issue affecting certificates, triggering automated invalidations/reinstalls/etc. This behavior was seen in our demo PC, which had CMOS battery issues. The immediate symptom was total failure of the Docker/Nvidia stack; additional indications include the system time being off and docker images being re-pulled each time instead of cached.

Usage¶

Manually Run Docker

These steps are taken care of by make docker-run in collect. Follow these steps for manual troubleshooting:

Ensure that X11 forwarding is correctly set (if this does not work, you may need to activate non-local X11 forwarding).
```
xhost +local:docker
```
Run the container with the requisite NVIDIA driver arguments, the desktop env variable, and the link to the unix X11 "file".
```
sudo docker run -it --privileged --net=host --env=NVIDIA_VISIBLE_DEVICES=all --env=NVIDIA_DRIVER_CAPABILITIES=all --env=DISPLAY --env=QT_X11_NO_MITSHM=1 -v /tmp/.X11-unix:/tmp/.X11-unix qoschatz/cartographer_ros /bin/bash
```
Mount any desired folders by adding
```
-v /{full_path_to_folder}:/{full_path_in_container}
```
NOTE: Pipe all processing results to a mounted directory to prevent outputs from being deleted when the docker container stops running.

The provided makefile assumes that there is a data folder (possibly symlinked) in the red-rover/processing directory, and that rosbags are already created using roverd rosbag. We provide the following commands:

make trajectory: compute poses.
make lidar: compute poses and output globally-aligned point clouds.
validate: alias for cartographer_rosbag_validate.
info: alias for rosbag info.

The following arguments are passed via environment variables:

DATASET: path to dataset, relative to the /data directory.
HEADLESS: whether to run cartographer in headless mode.

Tip

Use HEADLESS=true for unattended or remote operation.