Mixing Data-driven and Geometric Models for Satellite Docking Port State Estimation using an RGB or Event Camera

ICRA 2025

Cedric Le Gentil1, Jack Naylor2, Nuwan Munasinghe1, Jasprabhjit Mehami2, Benny Dai1, Mikhail Asavkin3, Donald G. Dansereau2 Teresa Vidal-Calleja1
1Robotics Institute, University of Technology Sydney 2Australian Centre for Robotics and School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney3ANT61
Paper arXiv Video Data
Lockheed Martin Mission Augmentation Port

We propose a lightweight, satellite-agnostic pipeline for docking port detection and pose estimation using standard or event-based monocular vision, enabling efficient and robust in-orbit servicing.

Abstract

In-orbit automated servicing is a promising path towards lowering the cost of satellite operations and reducing the amount of orbital debris. For this purpose, we present a pipeline for automated satellite docking port detection and state estimation using monocular vision data from standard RGB sensing or an event camera. Rather than taking snapshots of the environment, an event camera has independent pixels that asynchronously respond to light changes, offering advantages such as high dynamic range, low power consumption and latency. This work focuses on satellite-agnostic operations (only a geometric knowledge of the actual port is required) using the recently released Lockheed Martin Mission Augmentation Port (LM-MAP) as the target. By leveraging shallow data-driven techniques to preprocess the incoming data to highlight the LM-MAP’s reflective navigational aids and then using basic geometric models for state estimation, we present a lightweight and data-efficient pipeline that can be used independently with either RGB or event cameras.

Video

See an insight into our experimental setup, and data collection process in this video below.

BibTeX

@inproceedings{legentilicra2025,
        title={Mixing Data-driven and Geometric Models for Satellite Docking Port State Estimation using an RGB or Event Camera}, 
        author={Cedric Le Gentil and Jack Naylor and Nuwan Munasinghe and Jasprabhjit Mehami and Benny Dai and Mikhail Asavkin and Donald G. Dansereau and Teresa Vidal-Calleja},
        year={2025},
        booktitle={IEEE International Conference on Robotics and Automation},
        }

Acknowledgement

This project was supported by NSW Space Research Network Pilot Project RP220201. We also acknowledge the hard work of the technical staff at the ACFR in constructing our satellite analogue, without whom this work would not have been possible.