 Inverse depth parametrization generally improves numerical stability and allows to represent points with zero parallax.Moreover, the error associated to the observation of the point's position can be modelled with a Gaussian distribution when expressed in inverse depth.This is an important property required to apply methods, such as Kalman filters, that assume normality of the measurement error distribution.