Implementation of online photometric calibration (https://vision.in.tum.de/research/vslam/photometric-calibration)
Recent direct visual odometry and SLAM algorithms have demonstrated impressive levels of precision. However, they require a photometric camera calibration in order to achieve competitive results. Hence, the respective algorithm cannot be directly applied to an off-the-shelf-camera or to a video sequence acquired with an unknown camera. In this work we propose a method for online photometric calibration which enables to process auto exposure videos with visual odometry precisions that are on par with those of photometrically calibrated videos. Our algorithm recovers the exposure times of consecutive frames, the camera response function, and the attenuation factors of the sensor irradiance due to vignetting. Gain robust KLT feature tracks are used to obtain scene point correspondences as input to a nonlinear optimization framework. We show that our approach can reliably calibrate arbitrary video sequences by evaluating it on datasets for which full photometric ground truth is available. We further show that our calibration can improve the performance of a state-of-the-art direct visual odometry method that works solely on pixel intensities, calibrating for photometric parameters in an online fashion in realtime. For more details please refer to our paper.
If you use this code in your research, we would appreciate if you cite the respective publication.
Online Photometric Calibration of Auto Exposure Video for Realtime Visual Odometry and SLAM (P. Bergmann, R. Wang, D. Cremers), In IEEE Robotics and Automation Letters (RA-L), volume 3, 2018. [pdf] [video]
For more information on photometric calibration, see https://vision.in.tum.de/research/vslam/photometric-calibration.
Note: This is a preliminary release. You should consider this research code in beta. All interfaces are subject to change.
We support Ubuntu 14.04 and 16.04, and macOS, but it might work on a variety of other platforms that meet the dependency requirements.
The main dependencies are cmake 3.2 or later, a C++11 compiler, and OpenCV 2.4.
On Ubuntu 14.04 you need to get a more recent version of cmake.
sudo add-apt-repository ppa:george-edison55/cmake-3.x
Now continue to install dependencies like for Ubuntu 16.04.
sudo apt-get update sudo apt-get install \ build-essential \ g++ \ cmake \ libopencv-dev
CCache can help you speed up repeated builds.
Note: You need at least cmake version 3.4 for ccache to work automatically.
sudo apt-get install ccache
We assume you have installed Homebrew.
brew install cmake [email protected]
brew install ccache
Start in the package directory.
mkdir build cd build cmake .. make -j4
Optionally you can install the built libraries and executables.
sudo make install
Download sequence of the TUMmono VO dataset.
SEQ=30 wget http://vision.in.tum.de/mono/dataset/sequence_$SEQ.zip unzip sequence_$SEQ.zip unzip -d sequence_$SEQ/images sequence_$SEQ/images.zip
Run online calibration.
build/bin/online_pcalib_demo -i sequence_$SEQ/images --exposure-gt-file sequence_$SEQ/times.txt
Note: Currently the implementation is not suitable for fisheye-lenses with black borders around the image, which includes some of the TUMmono VO dataset sequences as well as the TUM VI dataset sequences.
Online calibration runs the code in a multithreaded way in parallel on the CPU. If tracking and backend optimization should be performed sequentially and real time performance is not required, the system can be run in batch calibration mode. For running in batch mode, simply add the command line option
For batch calibration you might want to use the exposure times from the optimization backend rather than the rapidly estimated exposure times from the frontend. In order to extract more keyframes to the backend optimizer, the run_settings parameters have to be adjusted.
These parameters can be changed by manually setting:
--nr-active-frames INT Maximum number of frames to be stored in the database. --keyframe-spacing INT Number of frames that keyframes are apart in the backend optimizer. --min-keyframes-valid INT Minimum number of frames a feature has to be tracked to be considered for optimization.
Photometric Calibration Usage: online_pcalib_demo [OPTIONS]
Options: -h,--help Print this help message and exit -i,--image-folder TEXT=images Folder with image files to read. --start-image-index INT=0 Start reading from this image index. --end-image-index INT=-1 Stop reading at this image index. --image-width INT=640 Resize image to this width. --image-height INT=480 Resize image to this height. --exposure-gt-file TEXT=times.txt Textfile containing ground truth exposure times for each frame for visualization. --calibration-mode TEXT=online Choose 'online' or 'batch' --nr-active-frames INT=200 Maximum number of frames to be stored in the database. --keyframe-spacing INT=15 Number of frames that keyframes are apart in the backend optimizer. --min-keyframes-valid INT=3 Minimum number of frames a feature has to be tracked to be considered for optimization.
This project was originally developed at the TUM computer vision group in 2017 by Paul Bergmann.
This project is available under a BSD 3-Clause license. See LICENSE.txt
Among others, we make use of the following libraries: