An Image Processing Pipeline for Autonomous Deep-Space Optical Navigation

TL;DR

This paper introduces an innovative image processing pipeline for autonomous deep-space optical navigation, enabling spacecraft to identify celestial beacons and determine their position without ground control, thus supporting future space exploration.

Contribution

It presents a novel pipeline utilizing the k-vector method and statistical likelihood for unresolved beacon recognition and line-of-sight extraction in deep space navigation.

Findings

Detection accuracy is independent of spacecraft position uncertainty.

Planet detection success rate exceeds 95% with known position within 10^5 km.

The pipeline enhances autonomous navigation capabilities for deep-space missions.

Abstract

A new era of space exploration and exploitation is fast approaching. A multitude of spacecraft will flow in the future decades under the propulsive momentum of the new space economy. Yet, the flourishing proliferation of deep-space assets will make it unsustainable to pilot them from ground with standard radiometric tracking. The adoption of autonomous navigation alternatives is crucial to overcoming these limitations. Among these, optical navigation is an affordable and fully ground-independent approach. Probes can triangulate their position by observing visible beacons, e.g., planets or asteroids, by acquiring their line-of-sight in deep space. To do so, developing efficient and robust image processing algorithms providing information to navigation filters is a necessary action. This paper proposes an innovative pipeline for unresolved beacon recognition and line-of-sight extraction from images for autonomous interplanetary navigation. The developed algorithm exploits the k-vector method for the non-stellar object identification and statistical likelihood to detect whether any beacon projection is visible in the image. Statistical results show that the accuracy in detecting the planet position projection is independent of the spacecraft position uncertainty. Whereas, the planet detection success rate is higher than 95% when the spacecraft position is known with a 3sigma accuracy up to 10^5 km.