On sensor pose parameterization for inertial aided visual SLAM

When appropriate infrastructure is not available, localization of pedestrians becomes a difficult task. This is especially the case in urban or indoor scenarios, where satellite navigation is hindered due to occlusions or multipath effects. A promising alternative is to combine a small, low-cost IMU with a camera in order to exploit the complementary error characteristics of these devices by simultaneously estimating the positions of observed landmarks and the trajectory of the sensor system with a stochastic filter. In this work, a standard approach to parameterize the error in position and attitude estimates that is commonly used in GNSS-INS integration is compared to alternative parameterizations that are based on the twist representation of rigid body motions, which has gained increasing popularity in the literature. For this purpose, the error-state transition and measurement equations are formulated for the twist representation as well as for the standard approach. Finally, the different approaches are compared on a simulated and a real indoor dataset by applying an extended Kalman filter (EKF).