为航天视觉导航生成适配机器学习的图像与元数据集
Training Datasets Generation for Machine Learning: Application to Vision Based Navigation
- 基于真实任务场景构建多源数据，融合仿真与实测
- 验证了SurRender与实验室设施生成的数据可支撑算法训练
- 适用于航天器对接与月球着陆等高精度导航任务
视觉导航通过摄像头提取图像信息实现精确制导、导航与控制。为推动机器学习在航天领域的应用，关键挑战在于验证现有训练数据集是否足够可靠。本文旨在生成适用于机器学习算法训练的图像与元数据集。选取两个典型应用场景：在轨卫星对接（以模拟卫星ENVISAT为目标）和月球着陆。数据来源包括嫦娥三号归档数据、德国DLR TRON实验室、空中客车机器人实验室、SurRender高保真图像仿真系统（结合模型捕获技术），以及生成对抗网络（GAN）。选定人工智能姿态估计算法和密集光流算法作为基准测试。结果表明，使用SurRender及选定实验室设施生成的数据集具备足够的质量与多样性，可有效支持机器学习算法的训练与验证。
原文摘要 · Abstract (English)
Vision Based Navigation consists in utilizing cameras as precision sensors for GNC after extracting information from images. To enable the adoption of machine learning for space applications, one of obstacles is the demonstration that available training datasets are adequate to validate the algorithms. The objective of the study is to generate datasets of images and metadata suitable for training machine learning algorithms. Two use cases were selected and a robust methodology was developed to validate the datasets including the ground truth. The first use case is in-orbit rendezvous with a man-made object: a mockup of satellite ENVISAT. The second use case is a Lunar landing scenario. Datasets were produced from archival datasets (Chang'e 3), from the laboratory at DLR TRON facility and at Airbus Robotic laboratory, from SurRender software high fidelity image simulator using Model Capture and from Generative Adversarial Networks. The use case definition included the selection of algorithms as benchmark: an AI-based pose estimation algorithm and a dense optical flow algorithm were selected. Eventually it is demonstrated that datasets produced with SurRender and selected laboratory facilities are adequate to train machine learning algorithms.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。