An Autonomous Vision-Based Algorithm for Interplanetary Navigation
Eleonora Andreis, Paolo Panicucci, Francesco Topputo
TL;DR
The paper addresses autonomous interplanetary navigation for CubeSats using a vision-based approach to replace ground-based radiometric tracking. It couples an image processing pipeline with a non-dimensional extended Kalman filter and a novel analytic measurement model that incorporates light-time and light-aberration effects. An optimal beacon selection strategy and on-board planet projection observations provide improved observability during deep-space transfers. Results on a high-fidelity Earth–Mars trajectory show end-of-leg position accuracy around 2,000 km and velocity accuracy around 0.5 m/s, with robustness to observation failures. The work demonstrates the feasibility of autonomous vision-based deep-space navigation and outlines pathways toward hardware-in-the-loop validation.
Abstract
The surge of deep-space probes makes it unsustainable to navigate them with standard radiometric tracking. Self-driving interplanetary satellites represent a solution to this problem. In this work, a full vision-based navigation algorithm is built by combining an orbit determination method with an image processing pipeline suitable for interplanetary transfers of autonomous platforms. To increase the computational efficiency of the algorithm, a non-dimensional extended Kalman filter is selected as state estimator, fed by the positions of the planets extracted from deep-space images. An enhancement of the estimation accuracy is performed by applying an optimal strategy to select the best pair of planets to track. Moreover, a novel analytical measurement model for deep-space navigation is developed providing a first-order approximation of the light-aberration and light-time effects. Algorithm performance is tested on a high-fidelity, Earth--Mars interplanetary transfer, showing the algorithm applicability for deep-space navigation.