Learning Cross-view Correspondences for Geo-localization on Planetary Surfaces
2026-06-29 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors address the problem of knowing exactly where a rover or explorer is on a planetary surface, like the Moon, where GPS doesn't work and wheel measurements become inaccurate over time. They created a new dataset of 360-degree panoramic ground images paired with overhead satellite-like views from a detailed Moon map to help computers learn to match the two perspectives. They tested a modern AI method designed to recognize places from different viewpoints and found it can work well for planetary surfaces. This suggests vision-based techniques could help with navigation in space exploration when traditional systems are unavailable.
geo-localizationplanetary explorationodometrycross-view matchinglunar terrainpanoramic imagestransformersatellite imageryrobot navigationdataset benchmark
Authors
Hong Minh Nguyen, Marcus Märtens, Tat-Jun Chin
Abstract
Maintaining global position awareness is a fundamental challenge for planetary surface exploration, since satellite-based positioning systems are unavailable and onboard odometry drifts over time. Although orbital mapping products, such as overhead imagery and terrain-derived maps, provide global context, aligning them with surface observations is challenging due to large viewpoint differences, low texture, repetitive terrain, and drastic changes in appearance caused by varying illumination and topography. We introduce a new cross-view geo-localization benchmark built from physically rendered surface panoramas and overhead tiles derived from a high-resolution lunar terrain model. Our dataset contains 10438 ground views rendered as 360$^\circ$ surface panoramas with matching overhead images precisely centered at the same location. Additionally, a set of overlapping tiles is provided to study off-center localization with multiple plausible candidates per panorama. We study the performance of a state-of-the-art transformer-based geo-localization method on our data, by training it from scratch and reporting retrieval accuracy. Our results demonstrate that learning-based cross-view localization methods can be successfully applied to the domain of planetary surfaces, providing a vision-based alternative to global navigation satellite systems.