At the beginning of my PhD I collaborated on fusing complementary sensorial information in the context of filter-based position and velocity estimation for our sFly micro aerial vehicles. The filter inputs are given by inertial measurements and camera poses obtained from a visual SLAM algorithm, however only up to an unknown scale-factor. The absolute scale is then recovered in a loosely coupled centripetal acceleration motion filter also containing the scale as an additional variable in the state. We furthermore investigated extensions to handle delayed, dropout-susceptible camera pose measurements.
I put a particular emphasis on the initialization of such Kalman filters. The convergence behavior of the estimated scale factor is largely depending on a good initial value. I therefore investigated deterministic ways for computing the scale and the gravity direction through short term integration of inertial measurements.
S Weiss, M W Achtelik, S Lynen, M C Achtelik, L Kneip, M Chli, and R Siegwart. Monocular Vision for Long-Term MAV State-Estimation: A Compendium. Journal of Field Robotics, Vol. 30, No. 5, pp. 803-831, 2013.
F Bourgeois, L Kneip, S Weiss, and R Siegwart. Delay and dropout tolerant state estimation for MAVs. In O Khatib, V Kumar, and G Sukhatme, editors, Experimental Robotics, volume 79 of Springer Tracts in Advanced Robotics, pages 571–584. Springer, 2014.
L Kneip, S Weiss, and R Siegwart. Deterministic Initialization of Metric State Estimation Filters for Loosely-Coupled Monocular Vision-Inertial Systems. In Proceedings of The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2011.
L Kneip, A Martinelli, S Weiss, D Scaramuzza, and R Siegwart. Closed-Form Solution for Absolute Scale Velocity Determination Combining Inertial Measurements and a Single Feature Correspondence. In Proceedings of The IEEE International Conference on Robotics and Automation (ICRA), 2011.
L Kneip, D Scaramuzza, and R Siegwart. On the Initialization of Statistical Optimum Filters with Application to Motion Estimation. In Proceedings of The IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2010.