For more information see https://vision.in.tum.de/dso
- 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 .
git clone https://github.com/IldarGreat/dso.git
docker build . -t ildarthegreat/dso
docker pull ildarthegreat/dso
docker pull ildarthegreat/dsoarm64
docker run \
-v {path}:/app/dso/build/set \
-e mode=2 \
... other environment (see Dockerfile)
ildarthegreat/dso (or ildarthegreat/dsoarm64)
path - it's folder that contains the calib file camera.txt and another folder named sequence that contains a sequence of images
Example: .../path
-camera.txt
-sequence
git clone https://github.com/IldarGreat/dso.git
Required. Install with
sudo apt-get install libsuitesparse-dev libeigen3-dev libboost-all-dev
Used to read / write / display images.
OpenCV is only used in IOWrapper/OpenCV/*. Without OpenCV, respective
dummy functions from IOWrapper/*_dummy.cpp will be compiled into the library, which do nothing.
The main binary will not be created, since it is useless if it can't read the datasets from disk.
Feel free to implement your own version of these functions with your prefered library,
if you want to stay away from OpenCV.
Install with
sudo apt-get install libopencv-dev
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
Or install like bellow
git clone --recursive https://github.com/stevenlovegrove/Pangolin.git -b v0.6
apt-get clean
sudo apt install libglew-dev
sudo apt-get install libegl1-mesa-dev
cd Pangolin
cmake -B build
cmake --build build
Used to read datasets with images as .zip, as e.g. in the TUM monoVO dataset. You can compile without this, however then you can only read images directly (i.e., have to unzip the dataset image archives before loading them).
sudo apt-get install zlib1g-dev
cd dso/thirdparty
tar -zxvf libzip-1.1.1.tar.gz
cd libzip-1.1.1/
./configure
make
sudo make install
sudo cp lib/zipconf.h /usr/local/include/zipconf.h # (no idea why that is needed).
After cloning, just run git submodule update --init to include this. It translates Intel-native SSE functions to ARM-native NEON functions during the compilation process.
cd dso
mkdir build
cd build
cmake ..
make -j4
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.
If you have error with boost, please add #include <boost/bind.hpp> to IndexThreadReduce
Run on a dataset from https://vision.in.tum.de/mono-dataset using
bin/dso_dataset \
files=XXXXX/sequence_XX/images.zip \
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.
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=XXXwhere XXX is either a folder or .zip archive containing images. They are sorted alphabetically. for .zip to work, need to comiple with ziplib support. You can use video_to_images.py in utils folder to split video into images. For example python video_to_images.py -v "path to video" . It create a folder 'images' desired sequence of images -
gamma=XXXwhere 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 or read this https://github.com/tum-vision/online_photometric_calibration -
vignette=XXXwhere XXX is a monochrome 16bit or 8bit image containing the vignette as pixelwise attenuation factors. See TUM monoVO dataset for an example. -
calib=XXXwhere XXX is a geometric camera calibration file. See below. If you want you can use calibration.py in utils it use chessboard 6*9, to check camera calibration settings. For example python calibration.py -i "path to images of chessboard"
fx fy cx cy 0
in_width in_height
"crop" / "full" / "none" / "fx fy cx cy 0"
out_width out_height
fx fy cx cy omega
in_width in_height
"crop" / "full" / "fx fy cx cy 0"
out_width out_height
fx fy cx cy k1 k2 r1 r2
in_width in_height
"crop" / "full" / "fx fy cx cy 0"
out_width out_height
fx fy cx cy k1 k2 k3 k4
in_width in_height
"crop" / "full" / "fx fy cx cy 0"
out_width out_height
(note: for backwards-compatibility, "Pinhole", "FOV" and "RadTan" can be omitted). See the respective
::distortCoordinates implementation in Undistorter.cpp for the exact corresponding projection function.
Furthermore, it should be straight-forward to implement other camera models.
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).
Rectification modes:
For image rectification, DSO either supports rectification to a user-defined pinhole model (fx fy cx cy 0),
or has an option to automatically crop the image to the maximal rectangular, well-defined region (crop).
full will preserve the full original field of view and is mainly meant for debugging - it will create black
borders in undefined image regions, which DSO does NOT ignore (i.e., this option will generate additional
outliers along those borders, and corrupt the scale-pyramid).
there are many command line options available, see main_dso_pangolin.cpp. some examples include
-
mode=X:mode=0use iff a photometric calibration exists (e.g. TUM monoVO dataset).mode=1use iff NO photometric calibration exists (e.g. ETH EuRoC MAV dataset).mode=2use iff images are not photometrically distorted (e.g. synthetic datasets).
-
preset=Xpreset=0: default settings (2k pts etc.), not enforcing real-time executionpreset=1: default settings (2k pts etc.), enforcing 1x real-time executionpreset=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) -
reverse=1: play sequence in reverse -
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.
Some parameters can be reconfigured from the Pangolin GUI at runtime. Feel free to add more.
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).
- 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.cppfor 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.
-
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.
Use a photometric calibration (e.g. using https://github.com/tum-vision/mono_dataset_code ).
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.
If your computer is slow, try to use "fast" settings. Or run DSO on a dataset, without enforcing real-time.
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.
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.