This repository contains the official implementation of the IEEE International Conference on Robotics and Automation (ICRA) 2024 paper:
Benchmarking Classical and Learning-Based Multibeam Point Cloud Registration,
Li Ling, Jun Zhang, Nils Bore, John Folkesson, Anna Wåhlin,
IEEE International Conference on Robotics and Automation (ICRA), 2024
The accompanying multibeam registration dataset DotsonEast can be accessed here.
For more information, please check out the project website
If you have any questions, feel free to contact us at:
- Li Ling ([email protected])
This repository contains the implementation for the MBES Dataset class and data loader, the classical methods GICP and FPFH, as well as the code for metrics computation and evaluations.
The code use to run the learning-based models are found in the following repository forks:
The dataset, pretrained models and evaluation results can be found here. Note that the dataset only contains the patches segmented according to the paper description. To construct a registration dataset, please consult main.py. If you want the exact data pairs and transforms as used in the paper, you can also extract these from the npz files containing in each method's evaluation results.
If you find this code useful for your work, please consider citing:
@inproceedings{ling2024benchmarking,
title={Benchmarking Classical and Learning-Based Multibeam Point Cloud Registration},
author={Ling, Li and Zhang, Jun and Bore, Nils and Folkesson, John and Wåhlin, Anna},
booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
year={2024},
organization={IEEE}
}In this project, we use part of the official implementations of the following work:
We thank the respective authors for open sourcing their work.