LDSO: Direct Sparse Odometry with Loop Closure
- LDSO: Direct Sparse Odometry with Loop Closure, X. Gao, R. Wang, N. Demmel, D. Cremers, In International Conference on Intelligent Robots and Systems (IROS), 2018.
- Direct Sparse Odometry, J. Engel, V. Koltun, D. Cremers, In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2018
- A Photometrically Calibrated Benchmark For Monocular Visual Odometry, J. Engel, V. Usenko, D. Cremers, In arXiv:1607.02555, 2016
There is a convenience script that will help you install the needed
libraries in Ubuntu 16.04 and later, including
On OSX you can install these via Homebrew.
Compile and install Pangolin for visualization.
This will build the thirdparty library and also ldso library for you. You can also follow the steps in this script manually (will compile DBoW3 and g2o first, and the ldso).
We provide examples on three datasets.
You can easily extend them in the
examples folder or add your own
executables for your camera or dataset.
After compilation, in the
bin directory there will be three
run_dso_xxx. You can either specify the directories
in the source file like
examples/run_dso_xxx.cc, or pass them as
command line parameters. When running LDSO, you will see a window
showing an visualization of camera trajectory and tracked points.
Make sure your working directory is at the root of LDSO code, to ensure that files like the BoW vocabulary file are found.
To run LDSO on TUM-Mono dataset sequence 34, execute:
./bin/run_dso_tum_mono \ preset=0 \ files=XXXXX/TUMmono/sequences/sequence_34/images.zip \ vignette=XXXXX/TUMmono/sequences/sequence_34/vignette.png \ calib=XXXXX/TUMmono/sequences/sequence_34/camera.txt \ gamma=XXXXX/TUMmono/sequences/sequence_34/pcalib.txt
To run LDSO on Kitti dataset sequence 00, execute:
./bin/run_dso_kitti \ preset=0 \ files=XXXXX/Kitti/odometry/dataset/sequences/00/ \ calib=./examples/Kitti/Kitti00-02.txt
To run LDSO on EuRoC dataset sequence MH_01_easy, execute:
./bin/run_dso_euroc \ preset=0 \ files=XXXX/EuRoC/MH_01_easy/mav0/cam0/
LDSO is a monocular VO based on DSO with Sim(3) loop closing function. Note we still cannot know the real scale of mono-slam. We only make it more consistent in long trajectories.
The red line in pangolin windows shows the trajectory before loop closing, and the yellow line shows the trajectory after optimization.
If you are looking for code instructions, take a look at
doc/notes_on_ldso.pdfand see if it can help you.
setting_enableLoopClosingto true/false to turn on/off loop closing function.
setting_fastLoopClosingwill record less data and make the loop closing faster.
If you need loop closing, please set
setting_pointSelection=1to make the program compute feature descriptors. If
setting_pointSelection=0, the program acts just like DSO, and
setting_pointSelection=2means random point selection, which is faster but unstable.
Some of the GUI buttons may not work.
You might also want to have a look DSO's README: https://github.com/JakobEngel/dso/blob/master/README.md
LDSO, like DSO, is licensed under GPLv3. It makes use of several third-party libraries. Among other's it comes with the source of: