Retriever

Point Cloud-based Place Recognition in Compressed Map

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

All the following commands should be run in retriever/

• 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}

• 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 of the checkpoint
• All the results can be visualized with: python scripts/vis_results.py
• The numbers of the paper are in experiments/perceiver_pn/default/version_15/checkpoints/oxford_evaluation_query.txt
• The pretrained model can be downloaded here here and should be placed into experiments/perceiver_pn/default/version_15/checkpoints/.

• The precompressed point cloud maps can be downloaded here.
• For the uncompressed point clouds, I refer to the PointNetVLAD.

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

@inproceedings{wiesmann2022icra,
author = {L. Wiesmann and R. Marcuzzi and C. Stachniss and J. Behley},
title = {{Retriever: Point Cloud Retrieval in Compressed 3D Maps}},
booktitle = {Proc.~of the IEEE Intl.~Conf.~on Robotics \& Automation (ICRA)},
year = 2022,
}