Geometry-aided Vision-based Localization of Future Mars Helicopters in Challenging Illumination Conditions

TL;DR

This paper introduces Geo-LoFTR, a geometry-aided deep learning model for robust Martian terrain image registration, enhancing localization accuracy for future Mars rotorcraft under challenging illumination conditions.

Contribution

The work presents a novel geometry-aided deep learning model and a simulation framework for improved map-based localization on Mars, addressing illumination challenges.

Findings

Geo-LoFTR outperforms prior models in localization accuracy.

The system is robust to large illumination and scale variations.

Validates effectiveness on real Mars imagery.

Abstract

Planetary exploration using aerial assets has the potential for unprecedented scientific discoveries on Mars. While NASA's Mars helicopter Ingenuity proved flight in Martian atmosphere is possible, future Mars rotorcraft will require advanced navigation capabilities for long-range flights. One such critical capability is Map-based Localization (MbL) which registers an onboard image to a reference map during flight to mitigate cumulative drift from visual odometry. However, significant illumination differences between rotorcraft observations and a reference map prove challenging for traditional MbL systems, restricting the operational window of the vehicle. In this work, we investigate a new MbL system and propose Geo-LoFTR, a geometry-aided deep learning model for image registration that is more robust under large illumination differences than prior models. The system is supported by a custom simulation framework that uses real orbital maps to produce large amounts of realistic images of the Martian terrain. Comprehensive evaluations show that our proposed system outperforms prior MbL efforts in terms of localization accuracy under significant lighting and scale variations. Furthermore, we demonstrate the validity of our approach across a simulated Martian day and on real Mars imagery. Code and datasets are available at: https://dpisanti.github.io/geo-loftr/.