Mixing Data-driven and Geometric Models for Satellite Docking Port State Estimation using an RGB or Event Camera
Cedric Le Gentil, Jack Naylor, Nuwan Munasinghe, Jasprabhjit Mehami,, Benny Dai, Mikhail Asavkin, Donald G. Dansereau, and Teresa Vidal-Calleja

TL;DR
This paper introduces a lightweight, data-efficient pipeline combining data-driven preprocessing and geometric models for satellite docking port detection and state estimation using RGB or event cameras, enhancing autonomous in-orbit servicing.
Contribution
It presents a novel hybrid approach that leverages shallow data-driven techniques with geometric models for satellite docking using monocular vision from RGB or event cameras.
Findings
The pipeline effectively detects docking ports with high accuracy.
It performs comparably with RGB and event camera modalities.
Quantitative tests validate the approach's robustness and efficiency.
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, etc. 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…
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Taxonomy
TopicsSpace Satellite Systems and Control · Optimization and Search Problems · Spacecraft Design and Technology
