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.
Highlights
Future lunar exploration missions are expected to rely on optical measurements to navigate independently of Earth-based operators
We suggest future investigation of invariants for patterns of lunar crater rims be focused on ease of computation or numerical stability rather than attempting to extract additional independent information from the crater rim contours
We provide the first comprehensive and mathematically rigorous approach for image-based crater identification using concepts from invariant theory
Summary
Future lunar exploration missions are expected to rely on optical measurements (e.g., images from a camera) to navigate independently of Earth-based operators. Recent decades have witnessed great technological advancement and expanding acceptance of autonomous vision-based navigation for the exploration of other celestial bodies (e.g., Moon, Mars, asteroids) These advancements in real-time, on-board OPNAV are exemplified by the technological progression from simple estimation of lander velocity with the Mars Exploration Rover’s DIMES system in 2004 [19] to autonomous feature tracking that will soon be demonstrated on the OSIRIS-REx mission to asteroid Bennu [105] and during landing of the Mars Perseverance (Mars 2020) rover [64]. These catalogs were built for planetary science purposes and not for image registration purposes—with the specific scientific question motivating the catalog construction often influencing the contents of the catalog This must be considered before repurposing a catalog for spacecraft navigation, since not all lunar crater catalogs are appropriate for building an index for crater pattern identification
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
