AI-Enabled Crater-Based Navigation for Lunar Mapping

2025-09-25

AI Summary

To address challenges in Crater-Based Navigation (CBN) for long-duration lunar orbital mapping—including sparse imagery, oblique viewing angles, and highly variable illumination—this paper proposes STELLA, the first end-to-end six-degree-of-freedom pose estimation system tailored for extended lunar missions. Methodologically, STELLA integrates Mask R-CNN-based crater detection, descriptor-free feature matching, robust Perspective-n-Circle (PnC) pose solving, and batch-wise orbital optimization, leveraging high-resolution digital elevation models (DEMs) and SPICE kernels to synthesize photorealistic simulated imagery. Key contributions include: (1) the first full-year-scale lunar CBN framework; (2) CRESENT-365—the first publicly available multi-view, illumination-variant, year-long lunar orbital dataset; and (3) the first demonstration of meter-level positioning and sub-degree attitude accuracy under realistic orbital mapping conditions, validated through comprehensive performance evaluation.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Crater-Based Navigation (CBN) uses the ubiquitous impact craters of the Moon observed on images as natural landmarks to determine the six degrees of freedom pose of a spacecraft. To date, CBN has primarily been studied in the context of powered descent and landing. These missions are typically short in duration, with high-frequency imagery captured from a nadir viewpoint over well-lit terrain. In contrast, lunar mapping missions involve sparse, oblique imagery acquired under varying illumination conditions over potentially year-long campaigns, posing significantly greater challenges for pose estimation. We bridge this gap with STELLA - the first end-to-end CBN pipeline for long-duration lunar mapping. STELLA combines a Mask R-CNN-based crater detector, a descriptor-less crater identification module, a robust perspective-n-crater pose solver, and a batch orbit determination back-end. To rigorously test STELLA, we introduce CRESENT-365 - the first public dataset that emulates a year-long lunar mapping mission. Each of its 15,283 images is rendered from high-resolution digital elevation models with SPICE-derived Sun angles and Moon motion, delivering realistic global coverage, illumination cycles, and viewing geometries. Experiments on CRESENT+ and CRESENT-365 show that STELLA maintains metre-level position accuracy and sub-degree attitude accuracy on average across wide ranges of viewing angles, illumination conditions, and lunar latitudes. These results constitute the first comprehensive assessment of CBN in a true lunar mapping setting and inform operational conditions that should be considered for future missions.

Problem

• Extending crater-based navigation to long-duration lunar mapping missions
• Addressing sparse oblique imagery under varying illumination conditions
• Developing robust pose estimation for year-long spacecraft operations

Innovation

• Uses Mask R-CNN-based crater detector for landmark identification
• Implements descriptor-less crater identification module for efficiency
• Combines perspective-n-crater solver with batch orbit determination