Lie Group Modelling for an EKF-Based Monocular SLAM Algorithm

This paper addresses the problem of monocular Simultaneous Localization And Mapping on Lie groups using fiducial patterns. For that purpose, we propose a reformulation classical camera model as matrix groups. Thus, define an original-state vector containing pose and set transformations from world frame to each pattern, which constitutes map’s state. Each element state, well pose, are intrinsically constrained evolve group SE(3). Filtering is then performed by extended Kalman filter dedicated solve visual SLAM process (LG-EKF-VSLAM). algorithm has been evaluated in different scenarios based simulated data real data. The results show LG-EKF-VSLAM can improve absolute position orientation accuracy, compared EKF (EKF-VSLAM).