Implementing LSD-SLAM, ORB-SLAM2, and LDSO with a custom dataset

This is the complementary materials of paper The Mapping Quality Evaluation of Monocular SLAM Solutions for Micro Aerial Vehicles.

There many existing visual SLAM benchmark datasets (e.g., EuRoC, TUM, and KITTI) to implement tests of SLAM algorithms. However, other than the above three popular datasets, implementing SLAM algorithms with a custom dataset needs some configuration work. LSD-SLAM, ORB-SLAM2, and LDSO are modified as follows to run with a custom image dataset created from videos collect by flying an AR. Drone 2.0.

Custom dataset

The custom dataset is created following the format of the EuRoC dataset. The images are sorted sequentially and stored in the data folder; a data.csv is created containing the timestamp and image filename information.

1. Record videos or ROS bag files.
   • Extract image frames from videos: ffmpeg -i xxx.mp4 -qscale:v 2 %06d.png
   • Extract image frames from ROS bags: rosbag2video

The created custom dataset should look like this:

YOURDATASET
├── data.csv
├── data
│   ├── 000001.png
│   ├── 000002.png
│   ├── 000003.png
│   ├── ... 

LSD-SLAM

The camera is calibrated with the input image dimension widthheight (640360). Because the height must to be a multiple of 16 to implement LSD-SLAM, the image dimension is “crop” to 640*352. The following file format works well:

562.87794/640 562.21464/360 327.27636/640 189.97880/360 0
640 360
crop
640 352

1. Run LSD-SLAM: rosrun lsd_slam_core dataset _files:=/xxx/data _hz:=30 _calib:=/pinhole_ardrone_calib.cfg.
2. Launch the lsd_slam viewer: rosrun lsd_slam_viewer viewer.
3. The generated point cloud map can be saved by entering “p” for a few seconds continuously when the lsd_slam_viewer window is selected. The point cloud map is saved as a pc.ply file in the lsd_slam_viewer folder.

The codes of lsd_slam :

lsd_slam
├── ...
├── ...
├── lsd_slam_core
│   ├── calib
│   │   ├── pinhole_ardrone_calib.cfg (new)
│   |   ├── ...
├── ...

ORB-SLAM2

It is straightforward to evaluate the ORB-SLAM algorithm by provided commands provided for all three datasets. To apply ORB-SLAM to a custom test dataset, the dataset is created following the format of the EuRoC dataset. The images are sorted sequentially and stored in the data folder; a data.csv is created containing the timestamp and image filename information. The “EuRoC.yaml” is modified to specify the camera calibration parameters of the AR. Drone camera and renamed as ardrone.yaml. A image timestamp file is also created as ardrone.txt and placed in the EuRoC_Time_Stamps folder. The following codes are modified or created to output generated point clouds as a file: System.cc, System.h, CMakeLists.txt, and mono_ardrone_pcl.cc.

Run ORB-SLAM2 with the created dataset: ./Examples/Monocular/mono_euroc_pcl Vocabulary/ORBvoc.txt Examples/Monocular/ardrone.yaml /xxx/data Examples/Monocular/EuRoC_TimeStamps/ardrone.txt.

• A point cloud file is saved as pointcloud.pcd in the ORB_SLAM2 folder.

The codes of orb_slam2:

orb_slam2
├── CMakeLists.txt (modified)
├── ...
├── src
│   ├── System.cc (modified)
│   ├── ...
├── include
│   ├── System.h (modified)
│   ├── ...
├── ...
├── ...
├── Examples
│   ├── Monocular
│   │   ├── ardrone.yaml (new)
│   │   ├── mono_ardrone_pcl.cc (new)
│   │   ├── mono_ardrone_pcl (new generated excutable)
│   │   ├── ...
│   │   ├── EuRoC_Time_Stamples
│   │   |   ├── ardrone.txt (new)

LDSO

Similar to ORB-SLAM, you can run LDSO algorithm by the provided commands with all three datasets. Your custom image dataset should be created similar to the EuRoC dataset.

1. Modify the camera calibration parameters of the EUROC.txt in LDSO/examples/EUROC.
   • Following the Radio-Tangential camera model, the calibration file is configured with format as follows:

     RadTan fx/in_width fy/in_height (cx+0.5)/in_width (cy+0.5)/in_height k1 k2 r1 r2
     in_width in_height
     "crop" / "full"  
     out_width out_height
     

   • Note: DSO requires a global shutter camera to achieve an acceptable results. The EUROC.txt in my LDSO is for Intel Realsense D435 camera rather than AR. Drone camera.
2. Run LDSO with the created dataset: ./bin/run_dso_euroc preset=0 files=/YOURDATASET
   • When the package finished running, the generated points cloud map will be automatically saved as the pointcloud.ply file after shutting down the Pangolin window.

Please cite this paper in your publications if it helps your research:

@article{wang2019mapping,
  title={Mapping Quality Evaluation of Monocular SLAM Solutions for Micro Aerial Vehicles.},
  author={Wang, J. and Shahbazi, M.},
  journal={ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
  volume = {XLII-2/W17},
  year={2019},
  pages = {413--420},
  URL = {https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-2-W17/413/2019/},
  DOI = {10.5194/isprs-archives-XLII-2-W17-413-2019} 
}