SPNv2: Robust Multi-task Learning and Online Refinement for Spacecraft Pose Estimation across Domain Gap

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

• [2022.05.28] Updated to include the implementations & results on SPNv2 with $\phi = 6$, GN layers.
• [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.
• [2023.07.25] There was a bug computing the translation vector from heatmaps. This bug does not affect the final results since we use translation vectors from the EfficientPose (i.e. regression) head. The bug is fixed (see the log), and the journal article in press will be updated soon.
• [2023.11.03] We are sharing the tangoPoints.mat file under core/utils/models.
• [2024.01.30] The binary masks are now available at this link.
• [2024.09.22] Updated the binary mask & pre-trained model links.

Introduction

This is the official PyTorch implementation of SPNv2.

Abstract

This work presents Spacecraft Pose Network v2 (SPNv2), a Convolutional Neural Network (CNN) for pose estimation of noncooperative spacecraft across domain gap. SPNv2 is a multi-scale, multi-task CNN which consists of a shared multi-scale feature encoder and multiple prediction heads that perform different tasks on a shared feature output. These tasks are all related to detection and pose estimation of a target spacecraft from an image, such as prediction of pre-defined satellite keypoints, direct pose regression, and binary segmentation of the satellite foreground. It is shown that by jointly training on different yet related tasks with extensive data augmentations on synthetic images only, the shared encoder learns features that are common across image domains that have fundamentally different visual characteristics compared to synthetic images. This work also introduces Online Domain Refinement (ODR) which refines the parameters of the normalization layers of SPNv2 on the target domain images online at deployment. Specifically, ODR performs self-supervised entropy minimization of the predicted satellite foreground, thereby improving the CNN’s performance on the target domain images without their pose labels and with minimal computational efforts.

Main Results

Offline Training of Full Configuration SPNv2 on SPEED+ HIL Domains and PRISMA25

		lightbox			sunlamp			prisma25
	E_T [m]	E_R [deg]	E_pose	E_T [m]	E_R [deg]	E_pose	E_T [m]	E_R [deg]	E_pose
$\phi$ = 3, BN	0.175	6.479	0.142	0.225	11.065	0.230	1.572	5.202	0.216
$\phi$ = 6, GN	0.216	7.984	0.173	0.230	10.369	0.219	1.570	9.014	0.283

ODR on SPEED+ HIL Domains

		lightbox			sunlamp
	E_T [m]	E_R [deg]	E_pose	E_T [m]	E_R [deg]	E_pose
$\phi$ = 3, BN (B = 4, N = 1024)	0.142	5.624	0.122	0.182	9.601	0.198
$\phi$ = 6, GN (N = 4096)	0.150	5.577	0.122	0.161	9.788	0.197

Installation

The code is developed and tested with python 3.7 and PyTorch 1.10 on Ubuntu 20.04. The offline training is tested on 4 NVIDIA V100 GPUs, whereas ODR is tested on a single NVIDIA GeForce RTX 2080 Ti 12GB GPU.

1. Install PyTorch.

2. Clone this repository at $ROOT.

3. Install dependencies.

pip3 install -r requirements.txt

4. Download SPEED+ at $DATASET.ROOT. The binary masks are available here link. Copy the masks folders under appropriate image domains.

5. (Optional) Download pre-trained models from here. Specify the model's path at ${TEST.MODEL_FILE}.

Pre-processing

To pre-process the training labels for the synthetic domain, run

python3 tools/preprocess.py --jsonfile synthetic/train.json --cfg experiments/offline_train_full_config.yaml

In addition to the .csv files under labels, it will save the resized images to images_768x512_RGB, for example. Include --no_masks to ignore masks and use keypoints to construct bounding boxes instead. Run the above with --no_labels to prepare list of images without labels.

Style Augmentation

The images can be stylized using the code from this repository.

Training & Testing

To train the full configuration SPNv2 on SPEED+ synthetic data on multiple GPUs:

python3 tools/train.py --cfg experiments/offline_train_full_config.yaml

To train on a single GPU, add --DIST.MULTIPROCESSING_DISTRIBUTED False. To train without binary masks, you must modify the MODEL configuration to the following:

HEAD:
    NAMES: ['heatmap', 'efficientpose']
    LOSS_HEADS: ['heatmap', 'efficientpose']
    LOSS_FACTORS: [1.0, 1.0]
    LOSS_NUMS: [1, 3]

This will skip building the Segmentation head altogether.

To test the trained model:

python3 tools/test.py --cfg experiments/offline_train_full_config.yaml

By default, it fetches the trained model from ${OUTPUT_DIR}/${MODEL.BACKBONE.NAME}/${EXP_NAME}/model_best.pth.tar. In order to test other trained models, specify its path at ${TEST.MODEL_FILE}. The testing is only supported for the synthetic validation set.

Training on Sherlock

If you are a Stanford student with access to the Sherlock cluster, check out the example job scripts at

• scripts/sherlock_preprocess.sh for pre-processing, and

• scripts/sherlock_offline_dist_train.sh for distributed training.

Online Domain Refinement (ODR)

To perform ODR, run

bash scripts/odr.sh

License

The SPNv2 repository is released under the MIT License (see LICENSE.md for more details).

Citation

@article{park2023spnv2,
	title = {Robust multi-task learning and online refinement for spacecraft pose estimation across domain gap},
	author = {Park, Tae Ha and D’Amico, Simone},
	journal = {Advances in Space Research},
	year = {2023},
	doi = {10.1016/j.asr.2023.03.036},
}