On Feature Parameterization for EKF-based Monocular SLAM

In the last years, the Monocular SLAM problem was widely studied, to allow the simultaneous reconstruction of the environment and the localization of the observer, by using a single camera. As for other SLAM problems, a frequently used feature for the representation of the world, is the 3D point. Differently from other SLAM problems, because of the perspective model of the camera, in Monocular SLAM, features cannot be completely perceived and initialized from a single measurement. To solve this issue, different parameterizations have been proposed in the literature, which try to solve also another problem in Monocular SLAM, i.e., the distortion of the Gaussian uncertainty in depth estimation that takes place because of the nonlinear measurement model. In this paper, we start from recent results in consistency analysis for these parameterizations to propose a novel approach to improve EKF-based Monocular SLAM even further. Our claims are sustained by an extended validation on simulated and real data.