Refining camera re-localization determination using prior pose model

Abstract

The present disclosure describes approaches to camera re-localization that improve the speed and accuracy with which pose estimates are generated by fusing output of a computer vision algorithm with data from a prior model of a geographic area in which a user is located. For each candidate pose estimate output by the algorithm, a game server maps the estimate to a position on the prior model (e.g., a specific cell on a heatmap-style histogram) and retrieves a probability corresponding to the mapped position. A data fusion module fuses, for each candidate pose estimate, a confidence score generated by the computer vision algorithm with the location probability from the prior model to generate an updated confidence score. If an updated confidence score meets or exceeds a score threshold, a re-localization module initiates a location-based application (e.g., a parallel reality game) based on the associated candidate pose estimate.