Autonomous, Bio-inspired vision-based navigation system for indoor flying using hybrid optical flow and stereopsis methods

Unmanned aerial vehicles (UAVs) have become an important part of today’s world. Micro Aerial Vehicles (MAVs) are small-sized UAVs that are suited for closed spaces, indoor or covert applications. Due to their small size and limited energy and load they can bear, there are several challenges in intelligent control of these vehicles. Also, due to their limitations, using energy-consuming sensors and complex control strategies which need high computational power, is not an option. To overcome MAV’s limitations, researchers are working on visual navigation systems. In this paper, we present an efficient strategy for the guidance and navigation of a 30gr MAV in indoor environments using a mixture of visual and inertial sensors. By taking inspiration from nature and flying insects, we combined optical flow and stereopsis methods to detect stationary obstacles and avoid collisions. Our control system uses two lateral cameras to avoid collision with lateral walls. To detect frontal walls and obstacles, instead of using optical flow methods, we use two frontal cameras with the same viewing direction and calculate the discrepancy between their images to detect walls and obstacles. For guidance, we designed a guidance system that functions well with our obstacle detection system and only needs destination and MAV’s current coordinates. By implementing our control system on a simulated coaxial helicopter, we were able to record more than 150 hours of collision-free flight and successful guidance to appointed destinations. Our control system is best suited for small sized UAVs in indoor environments and only needs inexpensive sensors and microcontrollers to implement