An event-based camera outputs an event whenever a change in scene brightness of a preset magnitude is detected at a particular pixel location in the sensor plane. The resulting sparse and asynchronous output coupled with the high dynamic range and temporal resolution of this novel camera motivate the study of event-based cameras for navigation and landing applications. However, the lack of real-world and synthetic datasets to support this line of research has limited its consideration for onboard use. This paper presents a methodology and a software pipeline for generating event-based vision datasets from optimal landing trajectories during the approach of a target body. We construct sequences of photorealistic images of the lunar surface with the Planet and Asteroid Natural Scene Generation Utility at different viewpoints along a set of optimal descent trajectories obtained by varying the boundary conditions. The generated image sequences are then converted into event streams by means of an event-based camera emulator. We demonstrate that the pipeline can generate realistic event-based representations of surface features by constructing a dataset of 500 trajectories, complete with event streams and motion field ground truth data. We anticipate that novel event-based vision datasets can be generated using this pipeline to support various spacecraft pose reconstruction problems given events as input, and we hope that the proposed methodology would attract the attention of researchers working at the intersection of neuromorphic vision and guidance navigation and control.

翻译：事件相机在传感器平面上检测到特定像素位置场景亮度变化达到预设阈值时，会输出一个事件。这种新型相机产生的稀疏异步输出，结合其高动态范围和高时间分辨率特性，促使研究者探索事件相机在导航与着陆应用中的潜力。然而，缺乏真实世界和合成数据集来支撑这一研究方向，限制了其机载应用的可行性。本文提出一套方法论和软件流水线，用于从目标天体接近过程中的最优着陆轨迹生成基于事件的视觉数据集。我们利用行星与小行星自然场景生成工具，沿一组通过改变边界条件获得的最优下降轨迹，在不同视角下构建月球表面的光电真实感图像序列。随后通过基于事件的相机模拟器将生成的图像序列转换为事件流。通过构建包含500条轨迹（附带事件流和运动场真值数据）的数据集，我们证明该流水线能够生成表面特征的逼真事件表征。预期该流水线可生成新型事件视觉数据集，支撑以事件为输入的航天器姿态重建问题研究，并希望所提出的方法论能吸引神经形态视觉与制导导航控制交叉领域研究者的关注。