SO-SLAM

This is Team 6's final project git repository for ROB530: Mobile Robotics.

The title of our project is Implementation and Evaluation of Semantic-Object SLAM Algorithm

The team members include: Ziqi Han, Zhewei Ye, Tien-Li Lin, Yi-Cheng Liu, Shubh Agrawal.

Related Paper: [RA-L&ICRA 2022]

• Liao Z, Hu Y, Zhang J, et al. So-slam: Semantic object slam with scale proportional and symmetrical texture constraints[J]. IEEE Robotics and Automation Letters, 2022, 7(2): 4008-4015. [PDF]

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📚 1. Prerequisites

sudo apt-get install libglew-dev
sudo apt-get install libeigen3-dev
sudo apt-get install libtbb-dev
sudo apt-get install libmetis-dev
sudo apt-get install libpugixml-dev
sudo apt-get install libpcl-dev

cmake 3.26.0
libboost 1.71.0  # make sure to compile C++ version from source code.
Pangolin 0.8.0
OpenCV 4.7.0

⚙️ 2. Compile GTSAM

Note that higher version may bring unexpected errors, we do not test other version so far.

git clone --branch 4.1.1 https://github.com/borglab/gtsam.git

Modify Eigen cmake config file: cmake/HandleEigen.cmake

set(GTSAM_USE_SYSTEM_EIGEN ON)

Then:

mkdir build
cd build
cmake ..
make check
sudo make install

🛠️ 3. Compile our repo

Branch Master contains point cloud visualization, so you have some more prerequisites.

git clone --branch master https://github.com/MRHan-426/SOSLAM.git

Branch 0.0.1 doesnot contain point cloud visualization, so you don't have to compile PCL, VTK.

git clone --branch 0.0.1 https://github.com/MRHan-426/SOSLAM.git

Then:

mkdir build
cd build
cmake ..
make

🌟 4. Examples

First, prepare dataset and rename as input directory. We provide three hand-labeled dataset below.

It cost us a lot of time to label and associate these datasets, so please star if we do help you.

There are detailed configurations in config.yaml, please change if you need.

• Dummy Example: We provide a demo to visualize for debugging. It will shows two hand designed ellipsoid.

./soslam_exe --dummy --3d

• Fr2 Desk: We provide a demo running dataset TUM RGBD. Download hand labeled dataset

./soslam_exe --Fr2_Desk --3d

• Fr1 Desk2: We provide a demo running dataset TUM RGBD. Download hand labeled dataset

./soslam_exe --Fr1_Desk2 --3d

• Fr2 Dishes: We provide a demo running dataset TUM RGBD. Download hand labeled dataset

./soslam_exe --Fr2_Dishes --3d

🎬 5. Videos and Documentation

• Our project presentation video is on [YouTube].

• Project Document: [PDF]

📝 6. Note

• If you want to use it in your work or with other datasets, you should prepare the dataset containing:

  • RGB image
  • Label xml (contain "objectKey" key to store the data association information)
  • Odom txt
  • Depth image (if you do not need point cloud visualization, just ignore)
  • Camera intrinsic txt

  Be aware that you should rename your images and xmls as number 1,2,3,...

  Be aware that RGB, Depth, Label, Odom must match.

• This is an incomplete version of our project.

  • We have a lot of experiments to be done.
  • We have not achieved real-time.

🏅 7. Acknowledgement

Thanks for the great work:

• SO-SLAM
• GTSAM
• Quadric-SLAM
• EAO-SLAM
• ORB-SLAM2
• YOLO-v8

📫 8. Contact

• Ziqi Han, Email: ziqihan@umich.edu
• Zhewei Ye, Email: yezhewei@umich.edu
• Tien-Li Lin, Email: tienli@umich.edu
• Yi-Cheng Liu, Email: liuyiche@umich.edu
• Shubh Agrawal, Email: shbhgrwl@umich.edu

Please cite the author's paper if you use the code in your work.

@article{liao2022so,
  title={So-slam: Semantic object slam with scale proportional and symmetrical texture constraints},
  author={Liao, Ziwei and Hu, Yutong and Zhang, Jiadong and Qi, Xianyu and Zhang, Xiaoyu and Wang, Wei},
  journal={IEEE Robotics and Automation Letters},
  volume={7},
  number={2},
  pages={4008--4015},
  year={2022},
  publisher={IEEE}
}

📌 9. Project Log & Todo List

• 1. Get dataset ready. Label and associate data by hand.

• 2. Rewrite quardicslam in C++, called "c++ core".

  • 2.1 Finish compiling.
  • 2.2 Get correct answer.

• 3.New Constraints and quadric Initialization.

  • 3.1 Finish writing constraints.
    • 3.1.1 Semantic Scale constraint.
    • 3.1.2 Plane Support constraint.
    • 3.1.3 Symmetry Texture constraint.
    • 3.1.4 Bounding Box constraint.
  • 3.2 Finish writing Quadric Initialization.
  • 3.3 Add Constraints into "c++ core".
    • 3.3.1 Debug Semantic Scale constraint.
    • 3.3.2 Debug Plane Support constraint.
    • 3.3.3 Debug Symmetry Texture constraint.
    • 3.3.4 Debug Bounding Box Constraint.
  • 3.4 Add Quadric Initialization part into "c++ core".

• 4. Add visualization part.

  • dependency problems.
  • adding quadrics drawing(MapDrawer).
    • Map object that can return all objects to draw.
    • camera pose drawing.
    • graph drawing.
    • verify drawing correctness.
  • adding frame drawer(FrameDrawer).
    • state == Tracking::NOT_INITIALIZED.
    • quadricimage is in Tracking, move to here.
    • It also use a Update to update images, find a way to place Update.
    • verify drawing correctness.
  • point cloud
  • multithread part
  • world axis
  • usability: control visualization

==========================================================================

4.10 ~ 4.13 TODO list

• 5. Finish debugging.

  • 5.1 Debug for the main optimization loop.
  • 5.2 Debug for symmetry factor.

• 6. Evaluation our algrithm. [3D point cloud to get ground truth]

  • 6.1 Rot.
    • basic Rot.
    • check association
    • check semantic table.
    • other dataset(annotation and binding)
  • 6.2 IOU.

• 7. Add visual odometry to take place of odom ground truth. [Orb Slam] [Optional]

==========================================================================

4.14 ~ 4.16 TODO list

• 8. Doing experiments, Record Video.
• 9. Write final report.
• 10. Modify code, make it clear.