In this paper, we present a collaborative path-planning framework for a group of micro aerial vehicles that are capable of localizing through vision. Each of the micro aerial vehicles is assumed to be equipped with a forward facing monocular camera. The vehicles initially use their captured images to build 3D maps through common features; and subsequently track these features to localize through 3D-2D correspondences. The planning algorithm, while connecting start locations to provided goal locations, also aims to reduce the localization uncertainty of the vehicles in the group. To achieve this, we develop a two-step planning framework: the first step attempts to build an improved map of the environment by solving the next-best-view problem for multiple cameras. We express this as a black-box optimization problem and solve it using the Covariance Matrix Adaption evolution strategy (CMA-ES). Once an improved map is available, the second stage of the planning framework performs belief space planning for the vehicles individually using the rapidly exploring random belief tree (RRBT) algorithm. Through the RRBT approach, the planner generates paths that ensure feature visibility while attempting to optimize path cost and reduce localization uncertainty. We validate our approach using experiments conducted in a high visual-fidelity aerial vehicle simulator, Microsoft AirSim.