深層学習とフォトリアル描画による宇宙機の姿勢推定
Deep Learning for Spacecraft Pose Estimation from Photorealistic Rendering
Unreal Engine 4製シミュレータURSOで学習データを生成し、方向の曖昧さをガウス混合で扱う深層学習により、単眼画像から宇宙機の6D姿勢を推定する手法を提案した。
著者: Pedro F. Proenca, Yang Gao
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
On-orbit proximity operations in space rendezvous, docking and debris removal require precise and robust 6D pose estimation under a wide range of lighting conditions and against highly textured background, i.e., the Earth. This paper investigates leveraging deep learning and photorealistic rendering for monocular pose estimation of known uncooperative spacecrafts. We first present a simulator built on Unreal Engine 4, named URSO, to generate labeled images of spacecrafts orbiting the Earth, which can be used to train and evaluate neural networks. Secondly, we propose a deep learning framework for pose estimation based on orientation soft classification, which allows modelling orientation ambiguity as a mixture of Gaussians. This framework was evaluated both on URSO datasets and the ESA pose estimation challenge. In this competition, our best model achieved 3rd place on the synthetic test set and 2nd place on the real test set. Moreover, our results show the impact of several architectural and training aspects, and we demonstrate qualitatively how models learned on URSO datasets can perform on real images from space.
