NeuralCoMapping

BUILD

conda create -p ./venv python=3.6
source activate ./venv
sh ./build.sh && python -m gibson2.utils.assets_utils --download_assets

DATASET

• Gibson

1. get dataset here

2. copy URL of gibson_v2_4+.tar.gz

3. run command

python -m gibson2.utils.assets_utils --download_dataset {URL}

• Matterport3D

1. get dataset according to README

2. run command

python2 download_mp.py --task_data igibson -o . `

3. move each folder of scenes to Gibson Dataset path

You can check Gibson Dataset path by running

python -m gibson2.utils.assets_utils --download_assets

USAGE

• Train

python main.py --global_lr 5e-4 --exp_name 'ma3_history' --critic_lr_coef 5e-2 --train_global 1 --dump_location train --scenes_file scenes/train.scenes

• Test (Example)

python main.py --exp_name 'eval_coscan_mp3dhq0f' --scenes_file scenes/mp3dhq0-f.scenes --dump_location std --num_episodes 10 --load_global best.global

• Test via scripts

python eval.py --load best.global --dataset mp3d --method rl -n 10

• Specify GPU Index

export CUDA_VISIBLE_DEVICES=3
export GIBSON_DEVICE_ID=4

Citation

If you find our work helpful in your research, please consider citing:

@InProceedings{Ye_2022_CVPR,
    author    = {Ye, Kai and Dong, Siyan and Fan, Qingnan and Wang, He and Yi, Li and Xia, Fei and Wang, Jue and Chen, Baoquan},
    title     = {Multi-Robot Active Mapping via Neural Bipartite Graph Matching},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {14839-14848}
}