KPPR: Exploiting Momentum Contrast for Point Cloud-Based Place Recognition

Installation

1. Install all requirements: pip install -r requirements.txt
2. Install this repository: pip install -e .

Usage

Training

All the following commands should be run in kppr/

• Please update the config files (especially the oxford_data.yaml to match your data_dir)
• Run the training: python train.py
• The output will be saved in retriever/experiments/{EXPERIMENT_ID}

Testing

• Test the model by running: python test.py --checkpoint {PATH/TO/CHECKPOINT.ckpt} --dataset {DATASET} --base_dir {PATH/TO/DATA}, where {DATASET} is e.g. oxford
• The output will be saved in the same folder as the checkpoint
• All the results can be visualized with: python scripts/vis_results.py
• The numbers of the paper are in experiments/kppr/.../oxford_evaluation_query.txt
• The pre-trained model can be downloaded here and should be placed into experiments/kppr/lightning_logs/version_0/.

Data

• The pre-compressed point cloud maps can be downloaded here and should be extracted to data/ (or simply put a symbolic link).
• For the uncompressed point clouds, I refer to PointNetVLAD.

Citation

If you use this library for any academic work, please cite the original paper.

@article{wiesmann2023ral,
author = {L. Wiesmann and L. Nunes and J. Behley and C. Stachniss},
title = {{KPPR: Exploiting Momentum Contrast for Point Cloud-Based Place Recognition}},
journal = ral,
volume = {8},
number = {2},
pages = {592-599},
year = 2023,
issn = {2377-3766},
doi = {10.1109/LRA.2022.3228174},
}