  Markerless Mobile Augmented Reality (AR) aims to anchor digital content in
the physical world without using specific 2D or 3D objects. Absolute Pose
Regressors (APR) are end-to-end machine learning solutions that infer the
device's pose from a single monocular image. Thanks to their low computation
cost, they can be directly executed on the constrained hardware of mobile AR
devices. However, APR methods tend to yield significant inaccuracies for input
images that are too distant from the training set. This paper introduces
KS-APR, a pipeline that assesses the reliability of an estimated pose with
minimal overhead by combining the inference results of the APR and the prior
images in the training set. Mobile AR systems tend to rely upon visual-inertial
odometry to track the relative pose of the device during the experience. As
such, KS-APR favours reliability over frequency, discarding unreliable poses.
This pipeline can integrate most existing APR methods to improve accuracy by
filtering unreliable images with their pose estimates. We implement the
pipeline on three types of APR models on indoor and outdoor datasets. The
median error on position and orientation is reduced for all models, and the
proportion of large errors is minimized across datasets. Our method enables
state-of-the-art APRs such as DFNetdm to outperform single-image and sequential
APR methods. These results demonstrate the scalability and effectiveness of
KS-APR for visual localization tasks that do not require one-shot decisions.
