Navigation of drones is predominantly based on sensor fusion algorithms. Most of these algorithms make use of some form of Bayesian filtering with a majority employing an Extended Kalman Filter (EKF), wherein inertial measurements are fused with a Global Navigation Satellite System (GNSS), and other sensors, in a kinematic framework to yield a navigation solution (position, velocity, attitude, and time). However, the long-term accuracy of this solution is exacerbated during the absence of satellite positioning, especially for small drones with low-cost MEMS inertial sensors. On the other hand, a recently proposed vehicle dynamic model (VDM)-based navigation system has shown significant improvement in positioning accuracy during the absence of a satellite positioning solution, although in a mostly offline setting. In this article, we present the software architecture of its real-time implementation using Robot Operating System (ROS) that separates and interfaces its core from a particular hardware. The presented implementation asynchronously handles different sensor data in a modular fashion and allows i) adapting the underlying aerodynamic model, ii) including complementary sensors, and iii) reducing the dimensionality of the EKF state space at run-time without compromising the navigation performance. The real-time performance of the proposed software architecture is evaluated during long GNSS absences of up to eight minutes and compared to that of inertial coasting.