氷衛星表面シミュレーションとステレオ深度推定によるサンプリング自律化
Icy Moon Surface Simulation and Stereo Depth Estimation for Sampling Autonomy
氷衛星の着陸機サンプリング自律化に向け、多様なステレオデータセットを生成するGUISSフレームワークを提案し、従来手法と深層学習によるステレオマッチングの深度推定性能を評価した。
著者: Ramchander Bhaskara, Georgios Georgakis, Jeremy Nash, Marissa Cameron, Joseph Bowkett, Adnan Ansar, Manoranjan Majji, Paul Backes
分類: cs.CV, cs.GR, cs.RO
原文アブストラクト
Sampling autonomy for icy moon lander missions requires understanding of topographic and photometric properties of the sampling terrain. Unavailability of high resolution visual datasets (either bird-eye view or point-of-view from a lander) is an obstacle for selection, verification or development of perception systems. We attempt to alleviate this problem by: 1) proposing Graphical Utility for Icy moon Surface Simulations (GUISS) framework, for versatile stereo dataset generation that spans the spectrum of bulk photometric properties, and 2) focusing on a stereo-based visual perception system and evaluating both traditional and deep learning-based algorithms for depth estimation from stereo matching. The surface reflectance properties of icy moon terrains (Enceladus and Europa) are inferred from multispectral datasets of previous missions. With procedural terrain generation and physically valid illumination sources, our framework can fit a wide range of hypotheses with respect to visual representations of icy moon terrains. This is followed by a study over the performance of stereo matching algorithms under different visual hypotheses. Finally, we emphasize the standing challenges to be addressed for simulating perception data assets for icy moons such as Enceladus and Europa. Our code can be found here: https://github.com/nasa-jpl/guiss.
