Monocular SLAM with Inverse Scaling Parametrization

The recent literature has shown that it is possible to solve t he monocular Simultaneous Localization And Mapping using both undelayed features ini- tialization and an Extedend Kalman Filter. The key concept, to achieve this result, was the introduction of a new parametrization calle d Unified Inverse Depth that produces measurements equations with a high degree of linear- ity and allows an efficient and accurate modeling of uncertai nties. In this paper we present a monocular EKF SLAM filter based on an altern ative parametrization, i.e., the Inverse Scaling Parametrizati on, characterized by a reduced number of parameters, a more linear measurement model, and a bet- ter modeling of features uncertainty for both low and high parallax features. Experiments in simulation demonstrate that the use of the Inverse Scaling solution improves the monocular EKF SLAM filter when compared with the Unified Inverse Depth approach, while experiments on real da ta show the system working as well.