GLAMAR: Geo-Location Assisted Mobile Augmented Reality for Industrial Automation

Abstract

Mobile Augmented Reality (MAR) is going to play an important role in industrial automation. In order to tag a physical object in the MAR world, a smart phone running MAR-based applications must know the precise location of an object in the real world. Tracking and localizing a large number of objects in an industrial environment can become a huge burden for the smart phone due to compute and battery requirements. In this paper we propose GLAMAR, a novel framework that leverages externally provided geo-location of the objects and IMU sensor information (both of which can be noisy) from the objects to 10-cate them precisely in the MAR world. GLAMAR offloads heavy-duty computation to the edge and supports building MAR-based applications using commercial development packages. We develop a regenerative particle filter and a continuously improving transformation matrix computation methodology to dramatically improve the positional accuracy of objects in the real and the AR world. Our prototype implementation on Android platform using ARCore shows the practicality of GLAMAR in developing MAR-based applications with high precision, efficiency, and more realistic experience. GLAMAR is able to achieve less then 10cm error compared to the ground truth for both stationary and moving objects and reduces the CPU overhead by 83% and battery consumption by 80% for mobile devices.