YOLOv8-Based Architecture for Pose Estimation of Uncooperative Spacecraft

Achieving autonomous spacecraft operations is crucial for missions such as On-Orbit Servicing (OOS) and Ac- tive Debris Removal (ADR). In this frame, this work fo- cuses on visual-based relative navigation by introducing a pose estimation architecture relying on the combina- tion of Convolutional Neural Networks (CNN) with state- of-the-art Perspective-n-Points (PnP) solvers. Specifically, a two-stage deep learning approach is proposed: in the first stage, a YOLOv8-based network effectively localizes the target allowing the selection of a region of interest; the second stage employs a specialized YOLOv8-pose net- work to detect and identify a set of 2D points on the im- age plane corresponding to natural features of the target's surface by solving a regression problem. The resulting set of 2D-3D point correspondences is input to an analytical PnP solver followed by a first pose refinement step rely- ing on the numerical solution of a least-squares problem through Gauss-Newton method and a subsequent addi- tional refinement based on the Levenberg-Marquardt al- gorithm. Performance assessment is carried out by train- ing and testing the CNNs and the entire processing pipeline on the SPEED+ dataset getting a mean rotation of 1.37° and a mean translation error of 4.7 cm.