DSO: Direct Sparse Odometry

For more information see https://vision.in.tum.de/dso

1. Related Papers

• Direct Sparse Odometry, J. Engel, V. Koltun, D. Cremers, In arXiv:1607.02565, 2016
• A Photometrically Calibrated Benchmark For Monocular Visual Odometry, J. Engel, V. Usenko, D. Cremers, In arXiv:1607.02555, 2016

Get some datasets from https://vision.in.tum.de/mono-dataset .

2. Installation

git clone https://github.com/eperdices/DSO

2.1 Dependencies

suitesparse and eigen3 (required).

Required. Install with

sudo apt-get install libsuitesparse-dev libeigen3-dev libboost-all-dev

OpenCV (required).

Used to read / write / display images.

Install with

sudo apt-get install libopencv-dev

Pangolin (highly recommended).

Used for 3D visualization & the GUI. Pangolin is only used in IOWrapper/Pangolin/*. You can compile without Pangolin, however then there is not going to be any visualization / GUI capability. Feel free to implement your own version of Output3DWrapper with your preferred library, and use it instead of PangolinDSOViewer

Install from https://github.com/stevenlovegrove/Pangolin

2.3 Build

	cd dso
	mkdir build
	cd build
	cmake ..
	make -j

this will compile a library libdso.a, which can be linked from external projects. It will also build a binary dso_dataset, to run DSO on datasets. However, for this OpenCV and Pangolin need to be installed.

3 Usage

Run on a dataset from https://vision.in.tum.de/mono-dataset using

	bin/dso_dataset \
		files=XXXXX/sequence_XX/ \
		calib=XXXXX/sequence_XX/camera.txt \
		gamma=XXXXX/sequence_XX/pcalib.txt \
		vignette=XXXXX/sequence_XX/vignette.png \
		preset=0 \
		mode=0

See https://github.com/JakobEngel/dso_ros for a minimal example on how the library can be used from another project. It should be straight forward to implement extentions for other camera drivers, to use DSO interactively without ROS.

3.1 Dataset Format.

The format assumed is that of https://vision.in.tum.de/mono-dataset. However, it should be easy to adapt it to your needs, if required. The binary is run with:

• files=XXX where XXX is a folder archive containing images. They are sorted alphabetically.

• gamma=XXX where XXX is a gamma calibration file, containing a single row with 256 values, mapping [0..255] to the respective irradiance value, i.e. containing the discretized inverse response function. See TUM monoVO dataset for an example.

• vignette=XXX where XXX is a monochrome 16bit or 8bit image containing the vignette as pixelwise attenuation factors. See TUM monoVO dataset for an example.

• calib=XXX where XXX is a geometric camera calibration file. See below.

Geometric Calibration File.

Calibration File for Radio-Tangential camera model

fx fy cx cy k1 k2 p1 p2 k3
width height

Explanation: Across all models fx fy cx cy denotes the focal length / principal point relative to the image width / height, i.e., DSO computes the camera matrix K as

	K(0,0) = width * fx
	K(1,1) = height * fy
	K(0,2) = width * cx - 0.5
	K(1,2) = height * cy - 0.5

For backwards-compatibility, if the given cx and cy are larger than 1, DSO assumes all four parameters to directly be the entries of K, and ommits the above computation.

That strange "0.5" offset: Internally, DSO uses the convention that the pixel at integer position (1,1) in the image, i.e. the pixel in the second row and second column, contains the integral over the continuous image function from (0.5,0.5) to (1.5,1.5), i.e., approximates a "point-sample" of the continuous image functions at (1.0, 1.0). In turn, there seems to be no unifying convention across calibration toolboxes whether the pixel at integer position (1,1) contains the integral over (0.5,0.5) to (1.5,1.5), or the integral over (1,1) to (2,2). The above conversion assumes that the given calibration in the calibration file uses the latter convention, and thus applies the -0.5 correction. Note that this also is taken into account when creating the scale-pyramid (see globalCalib.cpp).

3.2 Commandline Options

there are many command line options available, see main_dso_pangolin.cpp. some examples include

• mode=X:

  • mode=0 use iff a photometric calibration exists (e.g. TUM monoVO dataset).
  • mode=1 use iff NO photometric calibration exists (e.g. ETH EuRoC MAV dataset).
  • mode=2 use iff images are not photometrically distorted (e.g. synthetic datasets).

• preset=X

  • preset=0: default settings (2k pts etc.), not enforcing real-time execution
  • preset=1: default settings (2k pts etc.), enforcing 1x real-time execution
  • preset=2: fast settings (800 pts etc.), not enforcing real-time execution. WARNING: overwrites image resolution with 424 x 320.
  • preset=3: fast settings (800 pts etc.), enforcing 5x real-time execution. WARNING: overwrites image resolution with 424 x 320.

• nolog=1: disable logging of eigenvalues etc. (good for performance)

• nogui=1: disable gui (good for performance)

• nomt=1: single-threaded execution

• prefetch=1: load into memory & rectify all images before running DSO.

• start=X: start at frame X

• end=X: end at frame X

• speed=X: force execution at X times real-time speed (0 = not enforcing real-time)

• save=1: save lots of images for video creation

• quiet=1: disable most console output (good for performance)

• sampleoutput=1: register a "SampleOutputWrapper", printing some sample output data to the commandline. meant as example.

3.3 Runtime Options

Some parameters can be reconfigured from the Pangolin GUI at runtime. Feel free to add more.

3.4 Accessing Data.

The easiest way to access the Data (poses, pointclouds, etc.) computed by DSO (in real-time) is to create your own Output3DWrapper, and add it to the system, i.e., to FullSystem.outputWrapper. The respective member functions will be called on various occations (e.g., when a new KF is created, when a new frame is tracked, etc.), exposing the relevant data.

See IOWrapper/Output3DWrapper.h for a description of the different callbacks available, and some basic notes on where to find which data in the used classes. See IOWrapper/OutputWrapper/SampleOutputWrapper.h for an example implementation, which just prints some example data to the commandline (use the options sampleoutput=1 quiet=1 to see the result).

Note that these callbacks block the respective DSO thread, thus expensive computations should not be performed in the callbacks, a better practice is to just copy over / publish / output the data you need.

Per default, dso_dataset writes all keyframe poses to a file result.txt at the end of a sequence, using the TUM RGB-D / TUM monoVO format ([timestamp x y z qx qy qz qw] of the cameraToWorld transformation).

3.5 Notes

• the initializer is very slow, and does not work very reliably. Maybe replace by your own way to get an initialization.
• see https://github.com/JakobEngel/dso_ros for a minimal example project on how to use the library with your own input / output procedures.
• see settings.cpp for a LOT of settings parameters. Most of which you shouldn't touch.
• setGlobalCalib(...) needs to be called once before anything is initialized, and globally sets the camera intrinsics and video resolution for convenience. probably not the most portable way of doing this though.

4 General Notes for Good Results

Accurate Geometric Calibration

• Please have a look at Chapter 4.3 from the DSO paper, in particular Figure 20 (Geometric Noise). Direct approaches suffer a LOT from bad geometric calibrations: Geometric distortions of 1.5 pixel already reduce the accuracy by factor 10.

• Do not use a rolling shutter camera, the geometric distortions from a rolling shutter camera are huge. Even for high frame-rates (over 60fps).

• Note that the reprojection RMSE reported by most calibration tools is the reprojection RMSE on the "training data", i.e., overfitted to the the images you used for calibration. If it is low, that does not imply that your calibration is good, you may just have used insufficient images.

• try different camera / distortion models, not all lenses can be modelled by all models.

Photometric Calibration

Use a photometric calibration (e.g. using https://github.com/tum-vision/mono_dataset_code ).

Translation vs. Rotation

DSO cannot do magic: if you rotate the camera too much without translation, it will fail. Since it is a pure visual odometry, it cannot recover by re-localizing, or track through strong rotations by using previously triangulated geometry.... everything that leaves the field of view is marginalized immediately.

Computation Speed

If your computer is slow, try to use "fast" settings. Or run DSO on a dataset, without enforcing real-time.

Initialization

The current initializer is not very good... it is very slow and occasionally fails. Make sure, the initial camera motion is slow and "nice" (i.e., a lot of translation and little rotation) during initialization. Possibly replace by your own initializer.

5 ROS Execution

Building the node

1. Add the path including ROS/DSO to the ROS_PACKAGE_PATH environment variable. Replace PATH by the folder where you cloned DSO:

echo "export ROS_PACKAGE_PATH=\$ROS_PACKAGE_PATH:PATH/DSO/ROS" >> ~/.bashrc

2. Execute build_ros.sh script:

chmod +x build_ros.sh
./build_ros.sh

Running Node

DSO node reads RGB images from topic /camera/rgb/image_raw. You will need to add optional parameters (see examples above).

rosrun DSO DSO calib=XXX preset=0 mode=1

6 License

DSO was developed at the Technical University of Munich and Intel. The open-source version is licensed under the GNU General Public License Version 3 (GPLv3). For commercial purposes, we also offer a professional version, see http://vision.in.tum.de/dso for details.