Variable Observability Constrained Visual-Inertial-GNSS EKF-Based Navigation

Visual-Inertial Odometry (VIO) is an approach to give high-accuracy pose estimation in GNSS(Global Navigation Satellite System)-denied environments. However, visual-inertial navigation slowly drifts on its four unobservable directions, namely the translations and yaw in the world frame. The visual and inertial sensors are unable to provide information about how the world frame is aligned with global geographic coordinates. In this letter, g-MSCKF, a Multi-State Constraint Kalman Filter based approach, is developed to combine visual, inertial and GNSS raw measurements. With GNSS raw measurements, g-MSCKF has the ability to use GNSS information even when the number of satellites is below 4. An alignment filter between the local world frame and the global geographic frame is proposed and serves as the initializer for g-MSCKF. Furthermore, unobservable directions may exist and vary when the number of satellites, the satellite-receiver spatial geometry and the receiver motion follow specific patterns. Inconsistency of the estimator may happen under those variable-unobservable circumstances and a variable observability constrained method is provided to avoid inconsistency. Our algorithm is evaluated on real-world open datasets where the sensors traverse indoors and outdoors. The results show that our solution gives globally smooth trajectories in GNSS-intermittent situations with full exploitation of sensor potentials.