Work

Recurrent convolutional neural networks for LiDAR-based attitude initialization of rotating spacecraft

Year: 2026

Type: article

Abstract: Accurate attitude estimation is essential for autonomous in-orbit servicing and proximity operations. This work proposes a Recurrent Convolutional Neural Network (RCNN) used in coarse attitude initialization of known, possibly tumbling spacecraft using LiDAR-derived depth images. By processing temporal sequences of 2D point-cloud projections, the RCNN effectively handles symmetries, occlusions, and degraded sensing. Simulations across various spacecraft geometries, angular velocities, and ranges show that the RCNN yields lower initialization errors and higher convergence rates than conventional CNN baseline within the adopted experimental framework, with performance varying across angular velocity conditions.

Source: Control Engineering Practice

DOI: 10.1016/j.conengprac.2026.107134

Authors: Luca Bechis, Jean-Luc Sarvadon, Petre Ricioppo, Mauro Mancini

Institution: Politecnico di Torino

Language: English

Topic: Space Satellite Systems and Control

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,010

Funder: Ministero dell'Istruzione e del Merito