Abstract

Visual terrain-relative navigation (VTRN) is a localization method based on registering a source image taken from a robotic vehicle against a georeferenced target image. With high-resolution imagery databases of Earth and other planets now available, VTRN offers accurate, drift-free navigation for air and space robots even in the absence of external positioning signals. Despite its potential for high accuracy, however, VTRN remains extremely fragile to common and predictable seasonal effects, such as lighting, vegetation changes, and snow cover. Engineered registration algorithms are mature and have provable geometric advantages but cannot accommodate the content changes caused by seasonal effects and have poor matching skill. Approaches based on deep learning can accommodate image content changes but produce opaque position estimates that either lack an interpretable uncertainty or require tedious human annotation. In this…

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