This paper presents a distributed estimation and control architecture for leader-follower formations of multi-rotor micro aerial vehicles. The architecture involves multi-rate extended Kalman filtering and nonlinear model predictive control in order to optimize the system performance while satisfying various physical constraints of the vehicles, such as actuation limits, safety thresholds and perceptual restrictions. The architecture leverages exclusively onboard sensing, computation, and communication resources and it has been designed for enhanced robustness to perturbations thanks to its tightly coupled components. The architecture has been initially tested and calibrated in a high-fidelity robotic simulator and then validated with a real two-vehicle system engaged in formation navigation and reconfiguration tasks. The results not only show the high formation performance of the architecture while satisfying numerous constraints but also indicate that it is possible to achieve full navigation and coordination autonomy in presence of severe resource constraints as those characterizing micro aerial vehicles.