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Motivated by the goal of achieving long-term drift-free camera poseestimation in complex scenarios, we propose a global positioning frameworkfusing visual, inertial and Global Navigation Satellite System (GNSS)measurements in multiple layers. Different from previous loosely- and tightly-coupled methods, the proposed multi-layer fusion allows us to delicatelycorrect the drift of visual odometry and keep reliable positioning while GNSSdegrades. In particular, local motion estimation is conducted in theinner-layer, solving the problem of scale drift and inaccurate bias estimationin visual odometry by fusing the velocity of GNSS, pre-integration of InertialMeasurement Unit (IMU) and camera measurement in a tightly-coupled way. Theglobal localization is achieved in the outer-layer, where the local motion isfurther fused with GNSS position and course in a long-term period in aloosely-coupled way. Furthermore, a dedicated initialization method is proposedto guarantee fast and accurate estimation for all state variables andparameters. We give exhaustive tests of the proposed framework on indoor andoutdoor public datasets. The mean localization error is reduced up to 63%, witha promotion of 69% in initialization accuracy compared with state-of-the-artworks. We have applied the algorithm to Augmented Reality (AR) navigation,crowd sourcing high-precision map update and other large-scale applications.

Multi-layer VI-GNSS Global Positioning Framework with Numerical Solution aided MAP Initialization