ORB-based multiple fixed resolution approach for on-board visual recognition

Abstract

Maintenance and troubleshooting of hardware on a large scale pose a challenge in deploying expert technicians at multiple sites. Augmented Reality-based technology support equips the technicians with the skills they need to solve hardware problems even without expert level training, thereby reducing training time and cost to the vendor. Enabling Augmented Reality for technology support requires the ability to visually recognize the hardware in real time using mobile devices, and train the underlying algorithms at scale. This paper proposes a novel approach to address these issues. Our ORB-based fixed multi-resolution recognition algorithm achieves over 95% accuracy at a resolution scale of 0.2, and an approximately 60% faster recognition time than the next best comparable method. We also demonstrate the real-world applicability of our algorithm through an implementation of an Augmented Reality application.