LAUSR

Accurate pose estimation of planar objects is a key computation in visual localization tasks, with recent studies showing remarkable progress on a handful of baseline datasets. Nonetheless, achieving similar performance on sequences in unconstrained environments is still an ongoing quest to be accomplished, largely due to the existence of several sources of errors, which are correlated but often only partly tackled in the literature. In this article, we propose POP, a generic real-time planar-object pose-estimation framework which is designed to handle the aforementioned types of errors while not losing generality to a specific choice of keypoint detection or tracking algorithm. The essence of POP lies in activating keypoint detection module in the background as well as adding several refinement steps in order to reduce correlated sources of errors within the pipeline. We provide extensive experimental evaluations against state-of-the-art planar object tracking algorithms on baseline and more challenging datasets, empirically demonstrating the effectiveness of the POP framework for scenes with large environmental variations.