Global pose estimation using multi-sensor fusion for outdoor Augmented Reality

Abstract

Outdoor Augmented Reality typically requires tracking in unprepared environments. For global registration, Global Positioning System (GPS) is currently the best sensing technology, but its precision and update rate are not sufficient for high quality tracking. We present a system that uses Kalman filtering for fusion of Differential GPS (DGPS) or Real-Time Kinematic (RTK) based GPS with barometric heights and also for an inertial measurement unit with gyroscopes, magnetometers and accelerometers to improve the transient oscillation. Typically, inertial sensors are subjected to drift and magnetometer measurements are distorted by electro-magnetic fields in the environment. For compensation, we additionally apply a visual orientation tracker which is drift-free through online mapping of the unknown environment. This tracker allows for correction of distortions of the 3-axis magnetic compass, which increases the robustness and accuracy of the pose estimates. We present results of applying this approach in an industrial application scenario.