Global-LVBA

Global LiDAR-Visual Bundle Adjustment

Global-LVBA is a globally consistent LiDAR–Visual Bundle Adjustment system for refinement after LiDAR-inertial-visual odometry (e.g., FAST-LIVO2).
If you want to push accuracy to the next level, Global-LVBA further optimizes:

• point-cloud map consistency (reducing layering/stratification artifacts).
• camera poses towards pixel-level reprojection accuracy.

📬 For further assistance or inquiries, please feel free to contact Chunran Zheng at zhengcr@connect.hku.hk.

This figure shows qualitative comparisons before and after Global-LVBA refinement from both texture and geometric-structure perspectives. The initial poses are provided by FAST-LIVO2.

1. Prerequisites

• Python 3.8
• Ceres Solver 2.1.0
• Eigen 3.3.7
• OpenCV 4.2.0

2. Build

cd catkin_ws/src/Global-LVBA
git submodule update --init --recursive
cd SiftGPU && mkdir build && cd build && cmake .. && make
cd catkin_ws && catkin_make

3. Dataset preparation

Global-LVBA/
└── dataset/
    └── your_sequence_name/
        ├── all_image/
        │   ├── 1661398632.022152.png     # image named by timestamp
        │   ├── 1661398632.121881.png
        │   ├── ...
        │   └── image_poses.txt           # camera poses (timestamp-aligned)
        ├── all_pcd_body/
        │   ├── 1661398632.022152.pcd     # point cloud named by timestamp
        │   ├── 1661398632.121881.pcd
        │   ├── ...
        │   └── lidar_poses.txt           # LiDAR poses (timestamp-aligned)
        ├── colmap/
        │   ├── match.db                  # feature matching database
        │   └── sparse/                   # optional: COLMAP output for 3DGS
        │       └── 0/
        │           ├── images.txt
        │           └── points3D.txt
        ├── depth/                        # optional: generated depth maps
        └── reproj/                       # optional: reprojection error logs

4. Dataset download

The LVBA-Dataset (Google Drive) is generated from the raw rosbag in FAST-LIVO2-Dataset (Google Drive).

💡 Note: To prepare your own data with FAST-LIVO2, set pcd_save_en: false, type: 1, and img_save_en: true in the FAST-LIVO2 config to directly generate the required files for Global-LVBA.

5. Run our examples

Download the example data from LVBA-Dataset.

roslaunch Global-LVBA lvba.launch

💡 Note: We recommend using the .db file generated by COLMAP to obtain feature correspondences. The built-in SiftGPU extraction/matching in this repo is provided for convenience, but its matching quality is generally inferior to COLMAP.

6. License

The source code of this package is released under the MIT license.