SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap

This repository is developed by Tae Ha "Jeff" Park at Space Rendezvous Laboratory (SLAB) of Stanford University.

• [2021.12.02] Our paper will be presented at the 2022 IEEE Aerospace Conference! This repository is updated for our latest draft which will soon become available in arXiv.
• [2022.08.13] Citation update.
• [2022.10.12] As announced on the Kelvins website, the post-mortem competition will terminate on 2022/12/31, followed by the full release of the lightbox and sunlamp labels on 2023/01/01. This repository will be updated shortly afterwards to reflect the new availability of the test labels.
• [2023.01.12] The lightbox and sunlamp domain test labels are now available through Stanford Digital Repository! The metrics for HIL domains are also slightly modified to be in agreement with our new paper summarizing the competition.
• [2023.11.03] We are sharing the tangoPoints.mat file under src/utils.

Introduction

This is the official repository of the baseline studies conducted in our paper titled SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap. It consists of the official PyTorch implementations of the following CNN models:

• Keypoint Regression Network (KRN) [arXiv]
• Spacecraft Pose Network (SPN) [arXiv]

The implementation of the SPN model follows from the original work by Sumant Sharma based on Tensorflow and MATLAB. The repository also supports the following algorithms for the KRN model:

• Domain randomization via style augmentation introduced in Style Augmentation: Data Augmentation via Style Randomization by Jackson et al. (2019). The implementation derives from the official GitHub repository.
• Domain adaptation via DANN introduced in Domain-Adversarial Training of Neural Networks by Ganin et al. The implementation of Gradient Reversal Layer derives from the following GitHub repository.

Installation

The code is developed and tested with python 3.7 on Ubuntu 20.04. It is implemented with PyTorch 1.8.0 and trained on a single NVIDIA GeForce RTX 2080 Ti 12GB GPU.

1. Install PyTorch.

2. Clone this repository. Its full path ($PROJROOT) should be specified for --projroot in config.py.

3. Install dependencies:

pip install -r requirements.txt

4. Download SPEED+. Its full path ($DATAROOT) should be specified for --dataroot in config.py.

5. Place the appropriate CSV files under $DATAROOT/{domain}/splits_{model}/. For example, CSV files for synthetic training and validation sets for KRN should be placed under $DATAROOT/synthetic/splits_krn/. See below for creating CSV files for yourself.

6. Download the pre-trained AlexNet weights (bvlc_alexnet.npy) from here and place it under $PROJROOT/checkpoints/pretrained/ to be used for SPN.

Pre-processing

1. First, recover 11 keypoints as described in this paper. The order of keypoints does not matter as long as you are consistent with them. Save it as [3 x 11] array under the variable named tango3Dpoints and save it under src/utils/tangoPoints.mat. If you choose to save it elsewhere, make sure to specify its location w.r.t. $PROJROOT at --keypts_3d_model in config.py. Please note that we will not be releasing the keypoints.

2. For SPN, the attitude classes are provided at src/utils/attitudeClasses.mat.

3. Pre-processing can be done from preprocess.py. Specify the below arguments when running the script, which will convert the JSON file at $DATAROOT/{domain}/{jsonfile} to $DATAROOT/{domain}/{outcsvfile}.

Argument	Description
--model_name	KRN or SPN (e.g. krn)
--domain	Dataset domain (e.g. synthetic)
--jsonfile	JSON file name to convert (e.g. train.json)
--csvfile	CSV file to write (e.g. splits_krn/train.csv)

For example, to create CSV file of SPEED+ synthetic training set for KRN, run

python preprocess.py --model_name krn --domain synthetic --jsonfile train.json --csvfile splits_krn/train.csv

Training & Testing

Use below arguments to toggle on/off some settings:

Argument	Description
--no_cuda	Disable GPU training
--use_fp16	Use mixed-precision training
--randomize_texture	Perform style augmentation online during training
--perform_dann	Perform domain adaptation via DANN

Note the networks in this repository are not trained with mixed-precision training, but it's recommended if your GPU supports Tensor Cores to expedite the training.

To train KRN on SPEED+ synthetic training set:

python train.py --savedir 'checkpoints/krn/synthetic_only' \
                --logdir 'log/krn/synthetic_only' \
                --model_name 'krn' --input_shape 224 224 \
                --batch_size 48 --max_epochs 75 \
                --optimizer 'adamw' --lr 0.001 \
                --weight_decay 0.01 --lr_decay_alpha 0.95 \
                --train_domain 'synthetic' --test_domain 'synthetic' \
                --train_csv 'train.csv' --test_csv 'test.csv'

Add --randomize_texture to train with style augmentation.

To test KRN on synthetic validation images:

python test.py --pretrained 'checkpoints/krn/synthetic_only/model_best.pth.tar' \
               --logdir 'log/krn/synthetic_only' --resultfn 'results.txt' \
               --model_name 'krn' --input_shape 224 224 \
               --test_domain 'synthetic' --test_csv 'validation.csv'

which will write the test results to $PROJROOT/log/krn/synthetic_only/results.txt.

To test KRN on lightbox test images with DANN:

python adapt.py --savedir 'checkpoints/krn/dann_lightbox' \
                --logdir 'log/krn/dann_lightbox' --resultfn 'results.txt' \
                --model_name 'krn' --input_shape 224 224 \
                --batch_size 16 --max_epochs 750 --test_epoch 50 \
                --optimizer 'adamw' --lr 0.001 \
                --weight_decay 0.01 --lr_decay_alpha 0.95 --lr_decay_step 10 \
                --train_domain 'synthetic' --test_domain 'lightbox' \
                --train_csv 'train.csv' --test_csv 'test.csv' \
                --perform_dann

which currently assumes that test.csv for the lightbox domain is available with test labels for occasional validation. (You can comment out relevant parts in adapt.py to not run testing at all.)

License

The SPEED+ basline studies repository is released under the MIT License.

Citation

If you find this repository and the SPEED+ dataset helpful in your research, please cite the paper below along with the dataset itself.

@inproceedings{park2022speedplus,
  author={Park, Tae Ha and M{\"a}rtens, Marcus and Lecuyer, Gurvan and Izzo, Dario and D'Amico, Simone},
  booktitle={2022 IEEE Aerospace Conference (AERO)},
  title={SPEED+: Next-Generation Dataset for Spacecraft Pose Estimation across Domain Gap},
  year={2022},
  pages={1-15},
  doi={10.1109/AERO53065.2022.9843439}
}

KRN was introduced in the following paper:

@inproceedings{park2019krn,
	author={Park, Tae Ha and Sharma, Sumant and D'Amico, Simone},
	booktitle={2019 AAS/AIAA Astrodynamics Specialist Conference, Portland, Maine},
	title={Towards Robust Learning-Based Pose Estimation of Noncooperative Spacecraft},
	year={2019},
	month={August 11-15}
}

SPN was introduced in the following paper:

@inproceedings{sharma2019spn,
	author={Sharma, Sumant and D'Amico, Simone},
	booktitle={2019 AAS/AIAA Space Flight Mechanics Meeting, Ka'anapali, Maui, HI},
	title={Pose Estimation for Non-Cooperative Spacecraft Rendezvous Using Neural Networks},
	year={2019},
	month={January 13-17}
}