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

This branch is the Windows port of DM-VIO. No Windows specific APIs were introduced during porting, only the standard library equivalents of the *NIX APIs were used. It might still compile under Linux or MacOS, but I haven't verified either. It was only tested with Windows 10, Visual Studio 2019.

All dependecies (except for Pangolin) are gathered via Conan. They are listed in the conanfile.txt and all of them are available from the default conancenter repository. All following command line instructions are to be executed in Git Bash which comes with the official Windows Git installer. To install the dependencies, invoke conan from within the DM-VIO folder:

git clone https://github.com/AltVanguard/dm-vio.git -b windows_support
cd dm-vio
mkdir conan
cd conan
conan install ..

Like for DSO, Pangolin is used for the GUI. Checkout Pangolin v0.6 to a separate folder (outside DM-VIO). Same process for conan dependencies:

git clone https://github.com/AltVanguard/Pangolin.git -b v0.6
cd Pangolin
mkdir conan
cd conan
conan install ..

To build DM-VIO, you have to select installation folders for Pangolin and DM-VIO, then subsitute the folders into the following commands. I recommend not leaving the install folders as the default, and not installing the libraries under system folders or within the source repo folders. This avoids interference and you can easily restart from scratch if something goes wrong.

Build and install Pangolin first:

cd Pangolin
mkdir build
cd build
cmake .. \
	-DBUILD_EXAMPLES=OFF \
	-DBUILD_TESTS=OFF \
	-DBUILD_TOOLS=OFF \
	-DDISPLAY_X11=OFF \
	-DMSVC_USE_STATIC_CRT=OFF \
	-DCMAKE_INSTALL_PREFIX=<pangolin install folder> \
	-DCMAKE_MODULE_PATH=<pangolin repo folder>/conan/
# Here open the build/*.sln file, and build and install the solution from Visual Studio

Build DM-VIO:

cd dm-vio
mkdir build
cd build
cmake .. \
	-DCMAKE_INSTALL_PREFIX=<dm-vio install folder> \
	-DCMAKE_PREFIX_PATH=<pangolin install folder> \
	-DCMAKE_MODULE_PATH=<dm-vio repo folder>/conan/
# Here open the build/*.sln file, and build the solution from Visual Studio

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

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).