Factor Graph-Based Active SLAM for Spacecraft Proximity Operations

TL;DR

This paper presents a factor graph-based active SLAM approach for spacecraft proximity operations, where a chaser spacecraft actively controls its camera to improve localization and mapping accuracy in close-range space missions.

Contribution

It introduces an integrated planning and estimation framework using information-theoretic metrics for active perception in spacecraft SLAM, enhancing accuracy over passive methods.

Findings

Reduced uncertainty in spacecraft localization and mapping.

Higher accuracy compared to passive sensing strategies.

Effective joint planning and estimation demonstrated in simulations.

Abstract

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.