This is an implentation of "Point cloud structural similarity-based underwater sonar loop detection" which indicates detecting loops based on the structural similarity of point clouds generated from the data acquired by MBES.
- The script generate_data_from_antarctica.py relies on the AUVLib library.
- To run this script, follow the setup instructions provided in the AUVLib repository.
- All other scripts in this project are independent of AUVLib.
conda env create -f environment.yaml
conda activate PCSS- Download datasets
- Unzip the downloaded dataset
If you would like to generate the data yourself, follow the process outlined below. To process the Antarctica dataset, the auv_lib library must be installed. The antarctica_2019.cereal file is also required. You can download the file.
python generate_data_from_antarctica.pyor
python generate_data_from_seaward.py
# ex) python generate_data_from_seaward.py --data_id 3python execute.py
# ex) python execute.py --data_path data --data_id 1 --neighborhood_size 100 --score_threshold 2.95The codes and datasets in this repository are based on PointSSIM, Antarctica, and Seaward. Thanks to the authors of these codes and datasets.
@article{jung2024point,
title={Point Cloud Structural Similarity-based Underwater Sonar Loop Detection},
author={Jung, Donghwi and Pulido, Andres and Shin, Jane and Kim, Seong-Woo},
journal={arXiv preprint arXiv:2409.14020},
year={2024}
}