用可微渲染优化太空巡检轨迹,提升成像质量。
dLITE: Differentiable Lighting-Informed Trajectory Evaluation for On-Orbit Inspection
- 构建可微分仿真管道,联合优化轨道参数与光照条件。
- 自动设计非直观轨迹,显著提升近距离成像质量。
- 适合航天任务规划与视觉感知研究者参考。
对在轨空间资产的视觉检查日益受到航天器运营方关注,用于规划维护、评估损伤并延长高价值卫星寿命。低地球轨道(LEO)环境下,阳光在航天器表面的镜面反射、自阴影效应及动态光照显著影响整个轨道周期内数据采集质量。加之航天器间相对运动导致成像距离与姿态不断变化,进一步加剧挑战。现有巡检轨迹规划多依赖仿真,但针对提升近距离操作图像质量的轨迹优化仍处于空白。本文提出∂LITE,一种端到端可微分的在轨巡检仿真流程,融合先进可微分渲染工具与自研轨道传播器,实现基于视觉传感器数据的轨道参数联合优化。该方法可自动设计非直观巡检路径,大幅改善获取数据的质量与实用性。据我们所知,这是首个此类可微分巡检规划框架,为现代航天任务规划提供了新的计算范式。
原文摘要 · Abstract (English)
Visual inspection of space-borne assets is of increasing interest to spacecraft operators looking to plan maintenance, characterise damage, and extend the life of high-value satellites in orbit. The environment of Low Earth Orbit (LEO) presents unique challenges when planning inspection operations that maximise visibility, information, and data quality. Specular reflection of sunlight from spacecraft bodies, self-shadowing, and dynamic lighting in LEO significantly impact the quality of data captured throughout an orbit. This is exacerbated by the relative motion between spacecraft, which introduces variable imaging distances and attitudes during inspection. Planning inspection trajectories with the aide of simulation is a common approach. However, the ability to design and optimise an inspection trajectory specifically to improve the resulting image quality in proximity operations remains largely unexplored. In this work, we present $\partial$LITE, an end-to-end differentiable simulation pipeline for on-orbit inspection operations. We leverage state-of-the-art differentiable rendering tools and a custom orbit propagator to enable end-to-end optimisation of orbital parameters based on visual sensor data. $\partial$LITE enables us to automatically design non-obvious trajectories, vastly improving the quality and usefulness of attained data. To our knowledge, our differentiable inspection-planning pipeline is the first of its kind and provides new insights into modern computational approaches to spacecraft mission planning. Project page: https://appearance-aware.github.io/dlite/
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