bathy_nn_learning's Introduction

bathy_nn_learning

Introduction

This repository is intended for loop closure detection and feature matching in the context of Multibeam Echo Sounders (MBES).

Dependencies

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

Usage

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

Citation

If you find our work useful, please consider citing:

@article{tan2022data,
  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},
  journal={arXiv preprint arXiv:2209.08578},
  year={2022}
}