Initial Attitude Acquisition for Uncooperative Resident Space Objects Using Principal Lines

Space debris and its anticipated exponential growth in the coming years pose significant risks to future human space exploration. Active Debris Removal (ADR) missions are crucial for mitigating this growth by safely eliminating targets, either by redirecting them to denser atmospheric layers or graveyard orbits. The majority of these targets, referred to as Resident Space Objects (RSO), are uncooperative, lacking any control authority over their state. This presents formidable challenges for Guidance, Navigation, and Control (GNC) systems. Extensive academic efforts have been dedicated to solving the Relative Navigation problem, which focuses on estimating the relative pose between the chaser and target satellites. Existing solutions typically incorporate vision-based or LiDAR sensors, relying on knowledge of the object’s 3D shape and a successful co-registration between measured and on-board stored features. However, achieving accurate co-registration can be complex, particularly when the two feature sets are significantly distant in terms of attitude and position. Consequently, many proposed methods rely on relatively precise initial pose initialization to ensure their effectiveness. To address this issue, this paper introduces a novel methodology for acquiring the initial attitude without prior knowledge. The approach leverages the detection of lines as distinctive features, exploiting the invariant properties of line parameters regardless of whether the entire line or only a portion of it is detected. From the detected lines, the principal axis directions are estimated, assuming that the target bodies exhibit at least a partially rectangular shape, which is often the case for generic spacecraft. Subsequently, these principal axes are employed in a specific version of a Perspective-n-Line (PnL) method, specifically derived in this paper for the scenario where the reference lines in 3-D coincide with the body axes of the RSO. This Perspective-Principal-Lines (PPL) method yields eight potential attitude candidates, which are then validated through reprojections and verified using direct matches. The proposed methodology presents a promising approach for acquiring initial attitude estimates in ADR missions by eliminating the reliance on accurate prior attitude knowledge.