On orientation estimation using iterative methods in Euclidean space

This paper presents three iterative methods for orientation estimation. The first two are based on iterated Extended Kalman filter (IEKF) formulations with different state representations. The first is using the well-known unit quaternion as state (q-IEKF) while the other is using orientation deviation which we call IMEKF. The third method is based on nonlinear least squares (NLS) estimation of the angular velocity which is used to parametrise the orientation. The results are obtained using Monte Carlo simulations and the comparison is done with the non-iterative EKF and multiplicative EKF (MEKF) as baseline. The result clearly shows that the IMEKF and the NLS-based method are superior to q-IEKF and all three outperform the non-iterative methods.Funding agencies: Vinnova Industry Excellence Center LINK-SIC; Vinnova project "CYCLA - Cyclist Collision Avoidance Using Imagery Sensor"; CADICS, a Linnaeus Center; Swedish Research Council (VR)LINK-SIC, CADICS, CYCL