Principal Component Analysis for Lunar Crater Detection

Travis Driver; John A. Christian

cs.CVMay 16, 2026

Principal Component Analysis for Lunar Crater Detection

Authors: Travis Driver, John A. Christian

Organizations: Research Engineer II, School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332. · Associate Professor, School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA 30332.

Abstract

Optical navigation is a critical component for lunar orbiter and lander missions. Image-based crater identification has emerged as a promising technology for optical navigation due to the abundance of craters on the lunar surface and the availability of extensive crater catalogs. Moreover, due to the relative morphological homogeneity among lunar craters, template matching has been identified as a promising approach for identification. In this paper, we propose EigenCrater, an automated crater template generation method based on principal component analysis of crater digital elevation maps (DEMs). We demonstrate superior detection and position estimation performance relative to hand-picked templates on simulated lunar imagery.

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Deep Learning-Based Lunar Crater Terrain Relative Navigation

Batu Candan, Simone Servadio

Aerospace Engineering Department · Iowa State University · Ames, Iowa, USA

Accurate position estimation is crucial for the successful implementation of future lunar landings using autonomous vehicles, especially in dangerous environments with sparse terrain features. In this paper, we propose a terrain relative navigation (TRN) algorithm combining our deep-learning crater detector, which was designed specifically for the NASA Crater Detection Challenge problem, and an Extended Kalman Filter (EKF). Our detector analyzes crater features from the monocular images acquired from orbit, and their matches with craters from a global database are identified via a Hungarian assignment approach followed by the consensus-based outliers removal method. The estimated measurements are then used to refine an EKF, where spacecraft pose estimation in the Lunar-Centered Lunar-Fixed (LCLF) frame of reference, augmented with altitude aiding information, constrains radial drift. The simulation results indicate that even if the spacecraft is off from its actual location up to 5 km, TRN could recover from this situation, achieving navigation error reduction to a few hundred meters. It should be noted that in order to maintain crater feature correspondences, it is important to match the image resolution and the scales within the scene to the detector training set distribution.

Lunar Surface Spacecraft Extended Kalman Filter Terrain Navigation

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Abstract

Explore similar work

Deep Learning-Based Lunar Crater Terrain Relative Navigation

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