Deterministic initialization of metric state estimation filters for loosely-coupled monocular vision-inertial systems

In this work, we present a novel, deterministic closed-form solution for computing the scale factor and the gravity direction of a moving, loosely-coupled, and monocular vision-inertial system. The methodology is based on analysing delta-velocities. On one hand, they are obtained from a differentiation of the up-to-scale camera pose computation by a visual odometry or visual SLAM algorithm. On the other hand, they can also be retrieved from the gravity-affected short-term integration of acceleration signals. We derive a method for separating the gravity contribution and recovering the metric scale factor of the vision algorithm. The method thus also recovers the offset in roll and pitch angles of the vision reference frame with respect to the direction of the gravity vector. It uses only a single inertial integration period, and no absolute orientation information is required. For optimal sensor-fusion and metric scale-estimation filters in the loosely-coupled case, it has been shown that the convergence of the fusion of an up-to-scale pose information with inertial measurements largely depends on the availability of a good initial value for the scale factor. We show how this problem can be tackled by applying the method presented in this paper. Finally, we present results in simulation and on real data, demonstrating the suitability of the method in real scenarios.