DynoSAM Installation

January 19, 2026 ยท View on GitHub

Prerequisites

External dependancies (for visualization) not required for compilation.

GPU acceleration:

are now supported with the new Docker image. This provides support for TensorRT accellerated object-instance segmentation (which is now part of the DynoSAM pipeline) and CUDA acceleration in the front-end. Backwards compatability (i.e no CUDA support) is not currently a priority.

We provide detailed install instructions when using the Docker.

To install natively, install the dependancies as required by docker and build as a ROS2 package.

Docker Install instructions

DynoSAM has been tested on x86_64 and aarm64 (with a NVIDIA ORIN NX) devices using the two docker files provided. See the README.md for more detail on hardware used etc.

We provide scripts to build and create docker containers to run and develop DynoSAM which is intended to be mounted within the created container.

NOTE: with the current setup the embedded device only supports ROS Jazzy. The code should compile on either device without modification.

Folder Structure

To DynoSAM code to be changed within the docker environment, we mount the local version of the code within the container. To ensure this happens smoothly please download the DynoSAM code in the following structure

dynosam_pkg/
    DynoSAM/
    extras/
        rviz_dynamic_slam_plugins
        dynamic_slam_interfaces
    results/

where DynoSAM is this repository and anything in extras are other ROS packages that wish to be built alongside DynoSAM.

NOTE: `dynosam_pkg' may be any folder, its just a place to put all the DynoSAM related code.

NOTE: the reason for this structure is historical and due to the way the create_container scripts are written; you don't need to do this but I provide complete instructures for simplicity.

Docker Build

Build the relevant docker file:

cd dynosam_pkg/DynoSAM/docker &&
./build_docker_amd64.sh //or build_docker_l4t.sh

Create Container

Once built, you should have a docker image called acfr_rpg/dyno_sam_cuda or acfr_rpg/dynosam_cuda_l4t

In the associated create_container scripts modify the local variables to match the folder paths on the local machine. These folders will be mounted as a volume within the container

LOCAL_DATA_FOLDER=/path/to/some/datasets/
LOCAL_RESULTS_FOLDER=/path/to/dynosam_pkg/results/
LOCAL_DYNO_SAM_FOLDER=/path/to/dynosam_pkg/DynoSAM/
LOCAL_THIRD_PARTY_DYNO_SAM_FOLDER=/path/to/dynosam_pkg/extras

Run the creation script. The created container will be called dyno_sam

./create_container_amd64.sh //or ./create_container_l4t_jetpack6.sh

Finally enter into the container and build DynoSAM

cd /home/user/dev_w &&
export MAKEFLAGS="-j10" && clear && colcon build

CUDA dependancies

As of September 2025 we are adding cuda dependancies for certain OpenCV operations (and evenautually for running inference on images).

NOTE: I have a very old GPU (RTX 2080 which is only running CUDA 12.2 with Driver Version: 535.183.01), get CUDA support however you are able!

The docker file has been updated and based off the docker-ros-ml-images to include

  • CUDA
  • TensorRT
  • Pytorch built ontop of ROS2.

The CUDA dependancies are mostly for OpenCV modules but also eventually for adding DNN inference into DynoSAM itself so processing of images can be handled internally (ie YOLO).

Additional Installation Notes

dynamic_slam_interfaces is a require dependacy by default. This package is used to include custom messages that represet the state of each dynamic object per frame and is used by the ROS publishers.

To disable this dependancy compile the code as

colcon build --cmake-args -DENABLE_DYNAMIC_SLAM_INTERFACES=OFF

By default ENABLE_DYNAMIC_SLAM_INTERFACES=ON in the CMakeLists.txt. This CMake option will additionally change the visualisation (and the output topics) used by DynoSAM. See the ROS Visualisation section below.

Due to DynoSAM being build within ROS:

  • Need to build GTSAM with -DGTSAM_USE_SYSTEM_EIGEN=ON to avoid issues with ROS and OpenCV-Eigen compatability. Confirmed from https://discourse.ros.org/t/announcing-gtsam-as-a-ros-1-2-package/32739 which explains that the GTSAM ROS package is build with this flag set to ON (and describes it as "problematic"). We still want to build GTSAM from source so we can control the version and other compiler flags. Kimera-VIO's install instructions indicate that OpenGV must use the same version of Eigen as GTSAM, which can be set using compiler flags. Since we are using the ROS install Eigen, I have removed these flags and hope that the package manager with CMake can find the right (and only) version. This has not proved problematic... yet...

Possible Compilation Issues

Missing MPI Header Error

When first compiling DynoSAM, this error may appear as MPI relies on a non-existent directory. This issue should be fixed a patch in the dynosam_common CMakeLists.txt which directly updates the MPI_INCLUDE_PATH.

However, if the issue persists, the following is a known fix.

  1. Unset HPC-X variables 
    unset OPENMPI_VERSION
unset OMPI_MCA_coll_hcoll_enable
unset OPAL_PREFIX
export PATH=$(echo $PATH | tr ':' '\n' | grep -v '/opt/hpcx' | grep -v '/usr/local/mpi/bin' | paste -sd:)
    Now verify env | grep -i mpi echo $PATH that the OPAL_PREFIX and HPC-X paths no longer appear
  2. Install OpenMPI 
    sudo apt update
sudo apt install libopenmpi-dev openmpi-bin
    and verify that the following points to /usr/bin/mpicc 
    which mpicc
mpicc --version
  3. Clear the ROS2 workspace 
    rm -rf build/ install/ log/
  4. Build with the system MPI 
    export MPI_C_COMPILER=/usr/bin/mpicc
export MPI_CXX_COMPILER=/usr/bin/mpicxx
colcon build

Missing Dependencies

  • nlohmannjson may not be installed, so install with sudo apt install nlohmann-json3-dev
  • GTSAM may not be installed, so install with pip install gtsam