We investigate a scenario where a chaser spacecraft or satellite equipped with a monocular camera navigates in close proximity to a target spacecraft. The satellite's primary objective is to construct a representation of the operational environment and localize itself within it, utilizing the available image data. We frame the joint task of state trajectory and map estimation as an instance of smoothing-based simultaneous localization and mapping (SLAM), where the underlying structure of the problem is represented as a factor graph. Rather than considering estimation and planning as separate tasks, we propose to control the camera observations to actively reduce the uncertainty of the estimation variables, the spacecraft state, and the map landmarks. This is accomplished by adopting an information-theoretic metric to reason about the impact of candidate actions on the evolution of the belief state. Numerical simulations indicate that the proposed method successfully captures the interplay between planning and estimation, hence yielding reduced uncertainty and higher accuracy when compared to commonly adopted passive sensing strategies.

翻译：我们研究一种场景：配备单目相机的追踪航天器或卫星在目标航天器附近近距离导航。卫星的主要目标是利用可用的图像数据构建操作环境的表示，并在其中实现自身定位。我们将状态轨迹与地图估计的联合任务构建为基于平滑的同时定位与建图（SLAM）问题实例，其中问题的底层结构以因子图表示。不同于将估计与规划视为独立任务，我们提出通过控制相机观测来主动降低估计变量（航天器状态与地图路标点）的不确定性。这通过采用信息论度量来实现，以评估候选动作对置信状态演化的影响。数值模拟表明，所提方法成功捕捉了规划与估计之间的相互作用，与常用的被动感知策略相比，能够降低不确定性并提高精度。