A voxel-based multiview point cloud refinement method via factor graph optimization
Our proposed framework incorporates two innovative designs, namely the multi-scale voxel-grid optimization strategy and the quaternion-based factor graph optimization that employs a hybrid of point-to-plane and point-to-point factors.
- The multi-scale voxel-grid optimization strategy is mainly designed to address the problem that registration is highly non-convex and prone to getting stuck into local minima.
- The quaternion-based factor graph optimization refines the pose parameters in the tangent space by two factors, a robust constraint in structured environments provided by point-to-plane factors and a complementary constraint from non-structured surroundings provided by point-to-point factors.
The code was tested on Ubuntu 18.04.
- ROS melodic
- PCL version 1.11.0 (other versions may also work)
- ceres version 2.10 or above
- Eigen 3.3x or above
Clone this repository
git clone https://github.com/WuHao-WHU/EVPA.git
cd EVPA
catkin_make
source ./devel/setup.bash
roslaunch evpa point_cloud_refinement.launch
All parameter setting can be modified in .launch file, you can change the default setting regarding different scenarios.
You can test on the open-source TLS dataset: WHU-TLS, Robotic 3D Scan datasets, ETH Dataset.
The framework supports *.ply, *.pcd formats of point cloud data. You may need to transform other formats to the supported formats.
If you find out our code useful in your research, please cite
@InProceedings{10.1007/978-981-99-8432-9_19,
author="Wu, Hao and Yan, Li and Xie, Hong and Wei, Pengcheng and Dai, Jicheng and Gao, Zhao and Zhang, Rongling",
title="A Voxel-Based Multiview Point Cloud Refinement Method via Factor Graph Optimization",
booktitle="Pattern Recognition and Computer Vision",
year="2024",
publisher="Springer Nature Singapore",
address="Singapore",
pages="234--245",
}
Email: [email protected]
Do not hesitate to contact the authors if you have any question or find any bugs.