Online Supervised Training of Spaceborne Vision for Proximity Operations using Adaptive Kalman Filtering

Grunnleggende konsepter

The author presents an Online Supervised Training method to bridge the domain gap in spaceborne neural networks for spacecraft navigation during Rendezvous and Proximity Operations.

Sammendrag

The content discusses the challenges of training spaceborne neural networks due to limited resources and proposes a method to improve performance by training online with real flight images. The approach integrates a pose estimation NN into an adaptive unscented Kalman filter, demonstrating improved performance on diverse image domains. The experiments validate the effectiveness of Online Supervised Training in improving navigation filter errors and overall pose estimation capabilities.

Statistikk

"ViTPose-T/16 has 5.8M parameters."
"SPNv2-B3 performs best with 10.6M parameters."
"ViTPose-T/16 trained for 30 epochs on NVIDIA RTX 4090 GPU."
"AUKF uses AUKF state vector x = [α⊤ p⊤ ω⊤]⊤."
"ViTPose trained offline for different epochs is evaluated on SHIRT ROE1 and ROE2 trajectories."

Sitater

"The experiments show that OST can improve the NN performance on target image domains."
"Training a lightweight NN with heavy data augmentation biases it away from features specific to synthetic imagery."
"The proposed method aims to close the domain gap between synthetic training images and real flight images."

Viktige innsikter hentet fra

Online Supervised Training of Spaceborne Vision during Proximity Operations using Adaptive Kalman Filtering

by Tae Ha Park,... klokken arxiv.org 03-08-2024

https://arxiv.org/pdf/2309.11645.pdf

Dypere Spørsmål

How does the proposed Online Supervised Training method compare to traditional approaches in space robotics

The proposed Online Supervised Training method in space robotics offers a significant advancement over traditional approaches by addressing the challenge of domain gap between synthetic training images and actual flight images. Unlike conventional methods that rely solely on pre-trained models or offline training, this method leverages incoming flight images during Rendezvous and Proximity Operations (RPO) to train a pose estimation Neural Network (NN) online using an adaptive unscented Kalman filter. By integrating the NN as a measurement module into the navigation filter, pseudo-labels are generated from the filter's state estimates for Online Supervised Training (OST). This approach allows for real-time adaptation and improvement of the NN performance based on actual data received during operations.

What are the implications of limited computational resources on satellite processors for implementing such training methods

Limited computational resources on satellite processors present challenges for implementing complex training methods like Online Supervised Training. The scarcity of onboard GPU support and restricted access to space make it difficult to perform resource-intensive tasks such as deep learning model training in real-time. In this context, the proposed OST method addresses these limitations by optimizing training processes for efficiency. By utilizing lightweight neural network architectures with heavy data augmentation techniques, biasing networks away from specific features in synthetic imagery, and performing single backpropagation rounds on each image during OST, computational demands are minimized while still improving NN robustness across domain gaps.

How can the findings of this study be applied to other domains beyond space technology

The findings of this study have broader implications beyond space technology domains. The concept of Online Supervised Training using adaptive filtering techniques can be applied to various fields where real-time adaptation based on incoming data is crucial. For example:

Autonomous Vehicles: Implementing similar methodologies could enhance object detection systems' adaptability to changing environments.
Medical Imaging: Real-time updates based on new patient scans could improve diagnostic accuracy in medical imaging applications.
Manufacturing: Adaptive machine learning algorithms could optimize production processes by adjusting parameters dynamically based on sensor feedback.
By applying principles from this study to other domains, industries can benefit from more responsive and accurate AI systems tailored to evolving conditions without requiring extensive computational resources upfront.