Lunar Crater Identification in Digital Images

Abstract

It is often necessary to identify a pattern of observed craters in a single image of the lunar surface and without any prior knowledge of the camera’s location. This so-called “lost-in-space” crater identification problem is common in both crater-based terrain relative navigation (TRN) and in automatic registration of scientific imagery. Past work on crater identification has largely been based on heuristic schemes, with poor performance outside of a narrowly defined operating regime (e.g., nadir pointing images, small search areas). This work provides the first mathematically rigorous treatment of the general crater identification problem. It is shown when it is (and when it is not) possible to recognize a pattern of elliptical crater rims in an image formed by perspective projection. For the cases when it is possible to recognize a pattern, descriptors are developed using invariant theory that provably capture all of the viewpoint invariant information. These descriptors may be pre-computed for known crater patterns and placed in a searchable index for fast recognition. New techniques are also developed for computing pose from crater rim observations and for evaluating crater rim correspondences. These techniques are demonstrated on both synthetic and real images.

Autonomous and Earth-Independent Orbit Determination for a Lunar Navigation Satellite System

“…Crater estimation techniques have received significant renewed interest in the context of lunar exploration [47,48]. This technique has been especially considered for the case of lunar descent, where decimetre performances are usually targeted [16,49].…”

“…The work of Christian provides a linear least-square estimate for the general crater positioning estimation problem and will be used here [47]. Traditional crater identification algorithms have been studied [50], but machine learning and Artificial Intelligence (AI) present higher performances, popularly adopted by [16,47,51].…”

“…The ENU frame is highlighted on the projected equator in Figure 7. The ellipse relation between A i and C i , after appropriate identification by a machine learning algorithm [16,51], can be related through a series of projection transformations for the spacecraft located at a position r P [47].…”

Onboard State Estimation Around Didymos With Recurrent Neural Networks and Segmentation Maps

“…On the other hand, Hera will deploy relative feature tracking algorithms in combination with navigation filters for the most critical phases in the vicinity of Didymos [9], [13]. Finally, there also exist IP algorithms that use dynamic triangulation [14], [15] exploiting features such as planets or local features on the surface of a body, and extracting triads of lunar craters and image invariant features with onboard catalogs [16], to generate position and velocity navigation solutions.…”