Abstract

Introducing prior structure information into visual state estimation systems generally could improve the localization performance. In this letter, we aim to address the paradox between accuracy and efficiency in coupling the visual factors with the structure constraints. To this end, we present a cross-modality method that tracks a camera in a prior map modelled by the Gaussian Mixture Model (GMM). With the pose estimated by the front-end initially, the local visual observations and map components are associated efficiently, and the visual structure from the triangulation is refined simultaneously. By introducing the hybrid structure factors into the joint optimization, the camera poses are bundle-adjusted with the local visual structure. By evaluating our complete system, namely GMMLoc, on the public dataset and comparing it against the state-of-the-art vision-dominant state estimators, we show how our system can provide a centimeter-level localization accuracy with only trivial computational overhead.