This paper presents a new unified feature parametrization approach for monocular SLAM. The parametrization is based on the parallax angle and can reliably represent both nearby and distant features, as well as features in the direction of camera motion and features observed only once. A new bundle adjustment (BA) algorithm using the proposed parallax angle parametrization is developed and shown to be more reliable as compared with existing BA algorithms that use Euclidean XYZ or inverse depth parametrizations. A new map joining algorithm that allows combining a sequence of local maps generated using BA with the proposed parametrization, that avoids the large computational cost of a global BA, and can automatically optimize the relative scales of the local maps without any loss of information, is also presented. Extensive simulations and a publicly available large-scale real dataset with centimeter accuracy ground truth are used to demonstrate the accuracy and consistency of the BA and map joining algorithms using the new parametrization. Especially, since the relative scales are optimized automatically in the proposed BA and map joining algorithms, there is no need to compute any relative scales even for a loop more than 1km.