Install newest software image:
(https://developer.nvidia.com/sdk-manager)
Initialize system:
(https://developer.nvidia.com/embedded/learn/get-started-jetson-agx-orin-devkit)

Install basic tools:

sudo apt install -y curl nano wget git
sudo apt install -y terminator

Test docker is working:
```
sudo docker run --rm hello-world
```
Enable docker without sudo:
(https://docs.docker.com/engine/install/linux-postinstall/#manage-docker-as-a-non-root-user)

Enable GPU-access for docker building:

Run sudo nano /etc/docker/daemon.json and add:

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

Restart docker: sudo systemctl restart docker

Install vs-code:
(https://code.visualstudio.com/docs/setup/linux)

Test docker is working:

docker run --rm hello-world
docker run -it --rm --runtime=nvidia --network=host -e DISPLAY=$DISPLAY -v /tmp/.X11-unix/:/tmp/.X11-unix nvcr.io/nvidia/l4t-base:r36.2.0
docker run -it --rm --runtime=nvidia --network=host dustynv/onnxruntime:1.20-r36.4.0

Check cuda access in container:

python3 -c 'import torch; print(torch.cuda.is_available());'

Enable maximum performance mode:
```
sudo nvpmodel -m 0
sudo jetson_clocks
```

RPT installation

Build docker container:

docker build --progress=plain -f extras/jetson/dockerfile -t rapidposetriangulation .
./run_container.sh

Build rpt package inside container:

cd /RapidPoseTriangulation/swig/ && make all && cd ../tests/ && python3 test_interface.py && cd ..

Test with samples:

python3 /RapidPoseTriangulation/scripts/test_triangulate.py

Evaluate datasets:

python3 /RapidPoseTriangulation/scripts/test_skelda_dataset.py

ROS interface

Build docker container:

docker build --progress=plain -f extras/jetson/dockerfile_ros -t rapidposetriangulation_ros .
./run_container.sh