Lessons from a Space Lab - An Image Acquisition Perspective

  title={Lessons from a Space Lab - An Image Acquisition Perspective},
  author={Leo Pauly and Michele L. Jamrozik and Miguel Ortiz del Castillo and Olivia Borgue and Inderpreet Singh and Mohatashem Reyaz Makhdoomi and Olga-Orsalia Christidi-Loumpasefski and Vincent Gaudilli{\`e}re and Carol Mart{\'i}nez and Arunkumar Rathinam and Andreas M. Hein and Miguel Angel Olivares-M{\'e}ndez and Djamila Aouada},
The use of Deep Learning (DL) algorithms has improved the performance of vision-based space applications in recent years. However, generating large amounts of annotated data for training these DL algorithms has proven challenging. While synthetically generated images can be used, the DL models trained on synthetic data are often susceptible to performance degradation, when tested in real-world environments. In this context, the Interdisciplinary Center of Security, Reliability and Trust (SnT… 

A Survey on Deep Learning-Based Monocular Spacecraft Pose Estimation: Current State, Limitations and Prospects

The primary goal of this survey is to describe the currentDL-based methods for spacecraft pose estimation in a comprehensive manner, and to help define the limitations towards the effective deployment of DL-based spacecraft Pose estimation solutions for reliable autonomous vision-based applications.

Emulating On-Orbit Interactions Using Forward Dynamics Based Cartesian Motion

The paper presents a novel Hardware-In-the-Loop (HIL) emulation framework of on-orbit interactions using on-ground robotic manipulators. It combines Virtual Forward Dynamic Model (VFDM) for Cartesian

Evaluation of Position and Velocity Based Forward Dynamics Compliance Control (FDCC) for Robotic Interactions in Position Controlled Robots

In robotic manipulation, end-effector compliance is an essential precondition for performing contact-rich tasks, such as machining, assembly, and human-robot interaction. Most robotic arms are

A Spacecraft Dataset for Detection, Segmentation and Parts Recognition

    D. HoangBo ChenTat-Jun Chin
    Computer Science
    2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • 2021
The main contribution of this work is the development of the dataset using images of space stations and satellites, with rich annotations including bounding boxes of spacecrafts and masks to the level of object parts, which are obtained with a mixture of automatic processes and manual efforts.

Micro-object pose estimation with sim-to-real transfer learning using small dataset

This work presents a novel deep learning approach for 3D pose estimation of micro/nano-objects, particularly useful in regimes of limited experimental data, based on a generative adversarial network (GAN) model.

VisDA: The Visual Domain Adaptation Challenge

The 2017 Visual Domain Adaptation (VisDA) dataset and challenge, a large-scale testbed for unsupervised domain adaptation across visual domains, is presented and a baseline performance analysis using various domain adaptation models that are currently popular in the field is provided.

Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering

This paper presents a simulator built on Unreal Engine 4, named URSO, to generate labeled images of spacecraft orbiting the Earth, and proposes a deep learning framework for pose estimation based on orientation soft classification, which allows modelling orientation ambiguity as a mixture model.

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

SPEED+ is the next generation spacecraft pose estimation dataset with specific emphasis on domain gap, used in the second international Satellite Pose Estimation Challenge co-hosted by SLAB and the Advanced Concepts Team of the European Space Agency to evaluate and compare the robustness of spaceborne ML models trained on synthetic images.

Satellite Pose Estimation Challenge: Dataset, Competition Design, and Results

The main contribution of this article is the analysis of the submissions of the 48 competitors, which compares the performance of different approaches and uncovers what factors make the satellite pose estimation problem especially challenging.

Sim-to-Real 6D Object Pose Estimation via Iterative Self-training for Robotic Bin-picking

An iterative self-training framework for sim-to-real 6D object pose estimation to facilitate cost-effective robotic grasping and is able to improve robotic bin-picking success by 19.54%, demonstrating the potential of iterative sim- to-real solutions for robotic applications.

Deep Learning-based Spacecraft Relative Navigation Methods: A Survey

Leveraging Temporal Information for 3D Trajectory Estimation of Space Objects

This work presents a new temporally consistent space object 3D trajectory estimation from a video taken by a single RGB camera, using temporal convolution neural network that enforces the temporal coherence over the estimated 3D locations.