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.
Bag of World Anchors for Instant Large-Scale Localization
Fernando Reyes-Aviles,Philipp Fleck,D. Schmalstieg,Clemens Arth
Published 2023 in IEEE Transactions on Visualization and Computer Graphics
ABSTRACT
PUBLICATION RECORD
- Publication year
2023
- Venue
IEEE Transactions on Visualization and Computer Graphics
- Publication date
2023-10-02
- Fields of study
Medicine, Computer Science, Engineering
- Identifiers
- External record
- Source metadata
Semantic Scholar, PubMed
CITATION MAP
EXTRACTION MAP
CLAIMS
- No claims are published for this paper.
CONCEPTS
- No concepts are published for this paper.
REFERENCES
Showing 1-46 of 46 references · Page 1 of 1
CITED BY
Showing 1-2 of 2 citing papers · Page 1 of 1