The design of a reliable control and guidance system for aerial vehicles based only on visual information and Inertial Measurement Unit (IMU) data has many unsolved challenging problems, ranging from hardware and software development to pure control-theoretical issues. Theses issues have been addressed by proposing a bio-inspired adaptive autopilot for selflocalization, obstacles detection and control of small autonomous rotorcraft using optic flow and IMU measurements. This paper focuses particulary on the design of a hierarchical adaptive control system. Adaptive control tools have been used to recover the absolute aircraft velocities and real distances to obstacles. These estimates are then exploited by a multipurpose hierarchical controller for achieving various navigational tasks such as take-off, hovering, trajectory tracking and vertical landing. Furthermore, the asymptotic stability of the entire closed-loop system has been established using connected systems control theories. Simulation results over various ranges of the flight envelope illustrate that the proposed autopilot performs very well and allows a simulated rotorcraft UAV to achieve interesting flight behaviours.