Efficient integration of inertial observations into visual SLAM without initialization

The use of accelerometer and gyro observations in a visual SLAM implementation is beneficial especially in high dynamic situations. The downside of using inertial is that traditionally high prediction rates are required as observations are provided at high sample rates. An accurate orientation and velocity estimate must also be maintained at all times in order to integrate the inertial observations and correct for the effect of gravity. This paper presents a way to pre-integrate the high rate inertial observations without the need for an initial orientation or velocity estimate. This allows for a slower filter prediction rate and use of inertial observations when the initial velocity and attitude of the platform are unknown. Additionally the initial velocity and roll and pitch of the platform become observable over time and an estimate of these values is provided by the filter. An estimate of the gravity vector is also provided. Results are presented using a delayed state information smoother implementation however due to the linearity of the equations this technique can be applied to extended Kalman filter (EKF) implementations just as easily.