Paper | Video | Project Page

When using this project in academic work, please consider citing:

@article{stumberg22dmvio,
  author = {L. von Stumberg and D. Cremers},
  title = {{DM-VIO}: Delayed Marginalization Visual-Inertial Odometry},
  journal = {{IEEE} Robotics and Automation Letters ({RA-L})},
  year = {2022},
  volume = {7},
  number = {2},
  pages = {1408-1415},
  doi = {10.1109/LRA.2021.3140129}
}

• DM-VIO: Delayed Marginalization Visual-Inertial Odometry, L. von Stumberg and D. Cremers, In IEEE Robotics and Automation Letters (RA-L), volume 7, 2022
• Direct Sparse Visual-Inertial Odometry using Dynamic Marginalization, L. von Stumberg, V. Usenko and D. Cremers, In International Conference on Robotics and Automation (ICRA), 2018
• Direct Sparse Odometry, J. Engel, V. Koltun, D. Cremers, In TPAMI, vol. 40, 2018

git clone https://github.com/lukasvst/dm-vio.git

The following instructions have been tested with Ubuntu 20.04. The system is also known to work well on Ubuntu 16.04, 18.04 and MacOS Big Sur (only Intel Macs have been tested so far).

Required, install with

sudo apt-get install cmake libsuitesparse-dev libeigen3-dev libboost-all-dev libyaml-cpp-dev

On MacOS we recommend Homebrew to install the dependencies. It might be necessary to install [email protected] instead of the newest boost, in order for the used GTSAM version to work.

Build from source with

sudo apt install libtbb-dev
git clone https://github.com/borglab/gtsam.git
cd gtsam
git checkout a738529af9754c7a085903f90ae8559bbaa82e75
mkdir build && cd build
cmake -DGTSAM_POSE3_EXPMAP=ON -DGTSAM_ROT3_EXPMAP=ON -DGTSAM_USE_SYSTEM_EIGEN=ON ..
make -j
sudo make install

Used to read, write and display images. Install with

sudo apt-get install libopencv-dev

Like for DSO, this is used for the GUI. You should install v0.6. Install from https://github.com/stevenlovegrove/Pangolin

sudo apt install libgl1-mesa-dev libglew-dev pkg-config libegl1-mesa-dev libwayland-dev libxkbcommon-dev wayland-protocols
git clone https://github.com/stevenlovegrove/Pangolin.git
cd Pangolin
git checkout v0.6
mkdir build
cd build
cmake ..
cmake --build .
sudo make install

For running tests, install with git submodule update --init.

Used to read datasets with images as .zip. See src/dso/README.md for instructions.

After cloning, run git submodule update --init to include this.

cd dm-vio
mkdir build
cd build
cmake ..
make -j

This compiles dmvio_dataset to run DM-VIO on datasets (needs both OpenCV and Pangolin installed). It also compiles the library libdmvio.a, which other projects can link to.

The project is based on DSO and only has two additional dependencies with GTSAM and yaml-cpp. In case of problems with compilation we recommend trying to compile https://github.com/JakobEngel/dso first and seeing if it works.

Download a TUM-VI sequence (download in the format Euroc / DSO 512x512) at https://vision.in.tum.de/data/datasets/visual-inertial-dataset

bin/dmvio_dataset
    files=XXXX/datasetXXXX/dso/cam0/images              
    vignette=XXXX/datasetXXXX/dso/cam0/vignette.png
    imuFile=XXXX/datasetXXXX/dso/imu.txt
    gtFile=XXXX/datasetXXXX/dso/gt_imu.csv
    calib=PATH_TO_DMVIO/configs/tumvi_calib/camera02.txt
    gamma=PATH_TO_DMVIO/configs/tumvi_calib/pcalib.txt
    imuCalib=PATH_TO_DMVIO/configs/tumvi_calib/camchain.yaml
    mode=0
    use16Bit=1
    preset=0                                                        # use 1 for realtime
    nogui=0                                                         # use 1 to enable GUI
    resultsPrefix=/PATH_TO_RESULTS/
    settingsFile=PATH_TO_DMVIO/configs/tumvi.yaml
    start=2                                                         

Instead of typing this long command you can use the python tools.

We strongly recommend using the python-dm-vio tools published at: https://github.com/lukasvst/dm-vio-python-tools

They can be used to

• prepare the EuRoC and 4Seasons sequences for usage with DM-VIO.
• run on all (or some) sequences of EuRoC, TUM-VI and 4Seasons and gather the results.
• create a Python evaluation script for inspecting the results and generating the plots shown in the paper.

There are two types of commandline arguments:

1. Main arguments defined in main_dmvio_dataset.cpp (see parseArgument). Most of these are derived from DSO, so you can read src/dso/README.md for documentation on them.
2. Lots of additional settings are defined using the SettingsUtil. They can be set either using comandline or by placing them in the yaml file defined with the commandline argument settingsFile. All of them are printed to commandline when the program starts (and also into the file usedSettingsdso.txt). Most of these are documented in the header file they are defined in (see src/IMU/IMUSettings.h, src/IMUInitialization/IMUInitSettings.h).

To run on your own dataset you need

• to pass the folder containing files with files=...
• an accurate camera calibration! For tips on calibration and the format of camera.txt see src/dso/README.md.
• to set the mode=1 unless you have a photometric calibration (vignette.png and pcalib.txt).
• a file times.txt which contains exactly one timestamp for each image in the image folder.

When enabling IMU data you also need

• IMU calibration (transformation between camera and IMU) as a camchain.yaml. Note that only the field cam0/T_cam_imu and optionally the noise values are read from this file.
• a file containing IMU data. For each image it must contain an IMU 'measurement' with exactly the same timestamp. If the sensor does not output this, a fake measurement with this timestamp has to be interpolated in advance. The DM-VIO python tools contain a script to do this.
• You should also set the IMU noise values (see configs/tumvi.yaml, configs/euroc.yaml, and configs/4seasons.yaml). You can read them from an Allan-Variance plot (either computed yourself or taken from datasheet of IMU). Note that often times these values are too small in practice and should be inflated by a large factor for optimal results.

You can first set useimu=0 to try the visual-only system (basically DSO). If this does not work well for comparably slow motions, there is likely a problem with camera calibration which should be addressed first.

DM-VIO is based on Direct Sparse Odometry (DSO), which was developed by Jakob Engel at the Technical University of Munich and Intel. Like DSO, DM-VIO is licensed under the GNU General Public License Version 3 (GPLv3).