This repository is intended for loop closure detection and feature matching in the context of Multibeam Echo Sounders (MBES).
- Python 3.8
- PyTorch 1.10
- PyTorch Geometric 2.0
- opencv, wandb, shapely, open3d, AUVLib (original repo)
Install the June 2022 version of AUVLib here:
git clone -b extended_bm [email protected]:ignaciotb/auvlib.git
For details, please refer to requirements.txt
(for pip) or environment.yml
(for conda).
Recommended for baseline: PCL 1.10 or its python binding
Datasets are available for download here datasets
Step 1: Run scripts/parse_cereal.py
to parse cereal data.
Step 2.1 - 2.3: Run other scripts in scripts/
to create datasets.
Step 3: Run train.py
to train a model. (Modify param.py
properly.)
Step 4: Run scripts in test/
to evaluate the model.
.
├── data # datasets
│ ├── Circle100 # training set
│ │ ├── raw # raw training set
│ │ └── processed # processed training set
│ ├── Circle100Valid
│ │ └── ...
│ └── Circle100Test
│ └── ...
├── scripts # scripts for data processing
├── utils # utility functions
├── test # testing scripts
├── models.py # model implementation
├── dataset.py # dataset implementation
├── param.py # parameters and configurations
└── train.py # training script
If you find our work useful, please consider citing:
@inproceedings{tan2023data,
title={Data-driven loop closure detection in bathymetric point clouds for underwater {SLAM}},
author={Tan, Jiarui and Torroba, Ignacio and Xie, Yiping and Folkesson, John},
booktitle={2023 IEEE International Conference on Robotics and Automation (ICRA)},
pages={3131--3137},
year={2023},
organization={IEEE}
}
Part of the code is based on some examples in PyTorch Geometric.