RGBD-DSO Direct Sparse Odometry with RGB-D Cameras for Indoor Scenes
1. Related Papers
- RGB-D DSO: Direct Sparse Odometry with RGB-D Cameras for Indoor Scenes, Yuan Z, Cheng K, Tang J, Yang X, In IEEE Transactions on Multimedia, 2021
2. Installation
git clone https://github.com/HustCK/RGBD-DSO.git
2.1 Required Dependencies
2.1.1 Suitesparse
Install with
sudo apt-get install libsuitesparse-dev libboost-all-dev
2.1.2 Eigen3
Eigen 3.2.8, Follow Eigen Installation.
2.1.3 OpenCV
OpenCV 2.4.9, Follow OpenCV Installation.
2.1.4 Pangolin
Pangolin, Follow Pangolin Installation.
2.1.5 ziplib
Install with
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
2.2 Build
cd RGBD-DSO
mkdir build
cd build
cmake ..
make -j4
3. Usage
3.1 Dataset Format
Let's take TUM RGB-D as an example.
<sequence folder name>
|____________rgb
|____________depth
|____________associate.txt
If you are using other datasets, pleasr adjust the file directory and format as described above.
3.2 Run
If you use the same datasets as in this article, run it directly with the following instructions:
bin/dso_dataset \
files=<sequence folder name> \
calib=<RGB-D DSO path>/calib/<dataset name>/calib.txt \
preset=0 \
mode=1
For more details on configuration parameters, see Direct Sparse Odometry.
4. Acknowledgement
This work is implemented based on Direct Sparse Odometry. Thanks to J. Engel et al., who open source such excellent code for community.