Bag of World Anchors for Instant Large-Scale Localization

In this work, we present a novel scene description to perform large-scale localization using only geometric constraints.
Our work extends compact world anchors with a search data structure to efficiently perform localization and pose estimation of
mobile augmented reality devices across multiple platforms (e.g., HoloLens 2, iPad). The algorithm uses a bag-of-words approach
to characterize distinct scenes (e.g., rooms). Since the individual scene representations rely on compact geometric (rather than
appearance-based) features, the resulting search structure is very lightweight and fast, lending itself to deployment on mobile devices.
We present a set of experiments demonstrating the accuracy, performance and scalability of our novel localization method. In
addition, we describe several use cases demonstrating how efficient cross-platform localization facilitates sharing of augmented reality
experiences.