  State-of-the-art (SOTA) hierarchical localisation pipelines (HLoc) rely on
image retrieval (IR) techniques to establish 2D-3D correspondences by selecting
the $k$ most similar images from a reference image database for a given query
image. Although higher values of $k$ enhance localisation robustness, the
computational cost for feature matching increases linearly with $k$. In this
paper, we observe that queries that are the most similar to images in the
database result in a higher proportion of feature matches and, thus, more
accurate positioning. Thus, a small number of images is sufficient for queries
very similar to images in the reference database. We then propose a novel
approach, AIR-HLoc, which divides query images into different localisation
difficulty levels based on their similarity to the reference image database. We
consider an image with high similarity to the reference image as an easy query
and an image with low similarity as a hard query. Easy queries show a limited
improvement in accuracy when increasing $k$. Conversely, higher values of $k$
significantly improve accuracy for hard queries. Given the limited improvement
in accuracy when increasing $k$ for easy queries and the significant
improvement for hard queries, we adapt the value of $k$ to the query's
difficulty level. Therefore, AIR-HLoc optimizes processing time by adaptively
assigning different values of $k$ based on the similarity between the query and
reference images without losing accuracy. Our extensive experiments on the
Cambridge Landmarks, 7Scenes, and Aachen Day-Night-v1.1 datasets demonstrate
our algorithm's efficacy, reducing 30\%, 26\%, and 11\% in computational
overhead while maintaining SOTA accuracy compared to HLoc with fixed image
retrieval.
