Deep learning has become the gold standard for image processing over the past decade. Simultaneously, we have seen growing interest in orbital activities such as satellite servicing and debris removal that depend on proximity operations between spacecraft. However, two key challenges currently pose a major barrier to the use of deep learning for vision-based on-orbit proximity operations. Firstly, efficient implementation of these techniques relies on an effective system for model development that streamlines data curation, training, and evaluation. Secondly, a scarcity of labeled training data (images of a target spacecraft) hinders creation of robust deep learning models. This paper presents an open-source deep learning pipeline, developed specifically for on-orbit visual navigation applications, that addresses these challenges. The core of our work consists of two custom software tools built on top of a cloud architecture that interconnects all stages of the model development process. The first tool leverages Blender, an open-source 3D graphics toolset, to generate labeled synthetic training data with configurable model poses (positions and orientations), lighting conditions, backgrounds, and commonly observed in-space image aberrations. The second tool is a plugin-based framework for effective dataset curation and model training; it provides common functionality like metadata generation and remote storage access to all projects while giving complete independence to project-specific code. Time-consuming, graphics-intensive processes such as synthetic image generation and model training run on cloud-based computational resources which scale to any scope and budget and allow development of even the largest datasets and models from any machine. The presented system has been used in the Texas Spacecraft Laboratory with marked benefits in development speed and quality.
翻译:在过去十年中,深层学习已成为图像处理的黄金标准。与此同时,我们看到人们日益关注轨道活动,如卫星服务和碎片清除等取决于航天器之间近距离运行的轨道活动。然而,目前有两个关键挑战对利用深层学习进行基于视觉的在轨近距离作业构成重大障碍。首先,这些技术的有效实施依赖于一个有效的模型开发系统,该系统简化了数据整理、培训和评估。第二,标签化培训数据(目标航天器的图像)的缺乏阻碍了建立强有力的深层学习模型。本文展示了一种开源深层学习管道,专门为轨道上视觉导航应用程序开发,以应对这些挑战。我们工作的核心是两个在云层结构之上建立的定制软件工具,这些工具将模型开发过程的各个阶段连接在一起。第一个工具利用了Blender,即开放源3D图形工具,以生成具有可配置模型的合成培训数据(定位和方向)、照明条件、背景以及空间图像中常见的偏差。第二个工具是基于插件和滚动成本模型的流程模型,它提供了所有基于滚动和滚动成本的流程模型,同时提供所有基于模型的滚动和滚动模型的滚动模型的模型,同时提供有效的滚动模型的滚动模型的模型,并提供了用于滚动的模型的滚动数据生成模型生成模型和滚动模型的模型的模型的模型。