This paper presents a novel control strategy, which the authors call optiPilot, for autonomous flight in the vicinity of obstacles. Most existing autopilots rely on a complete 6-degree-of-freedom state estimation using a GPS and an inertial measurement unit (IMU) and are unable to detect and avoid obstacles. This is a limitation for missions such as surveillance and environment monitoring that may require near-obstacle flight in urban areas or mountainous environments. OptiPilot instead uses optic flow to estimate proximity of obstacles and avoid them. Their approach takes advantage of the fact that, for most platforms in translational flight (as opposed to near-hover flight), the translatory motion is essentially aligned with the aircraft main axis. This property allows us to directly interpret optic flow measurements as proximity indications. They take inspiration from neural and behavioural strategies of flying insects to propose a simple mapping of optic flow measurements into control signals that requires only a lightweight and power-efficient sensor suite and minimal processing power. In this paper, they first describe results obtained in simulation before presenting the implementation of optiPilot on a real flying platform equipped only with lightweight and inexpensive optic computer mouse sensors, MEMS rate gyroscopes and a pressure-based airspeed sensor. They show that the proposed control strategy not only allows collision-free flight in the vicinity of obstacles, but is also able to stabilise both attitude and altitude over flat terrain. These results shed new light on flight control by suggesting that the complex sensors and processing required for 6 degree-of-freedom state estimation may not be necessary for autonomous flight and pave the way toward the integration of autonomy into current and upcoming gram-scale flying platforms.