Micro Aerial Vehicles (FOV) are very suitable for flying in indoor environments, but autonomous navigation is challenging due to their strict hardware limitations. This paper presents a highly efficient computer vision algorithm called Edge-FS for the determination of velocity and depth. It runs at 20 Hz on a 4 g stereo camera with an embedded STM32F4 microprocessor (168 MHz, 192 kB) and uses edge distributions to calculate optical flow and stereo disparity. The stereo-based distance estimates are used to scale the optical flow in order to retrieve the drone's velocity. The velocity and depth measurements are used for fully autonomous flight of a 40 g pocket drone only relying on on-board sensors. This method allows the MAV to control its velocity and avoid obstacles.
To design extremely lightweight MAV s for autonomous flight, some researchers looked into EMD sensors @cite_6 and other 1D signal sensors @cite_4 . @cite_8 proposed the design of a 45 g quadcopter for optical flow based control with 1D flow sensors. They followed up with this research on a heavier 278 g platform containing 8 of these sensors pointing in all directions @cite_14 . With this they could hover the quadcopter in various cluttered environments. The results are impressive, nevertheless they were achieved by using multiple single purpose sensors. As they can only sense motion, it does not leave much room to detect other variables necessary for navigation.
@cite_14 @cite_4 @cite_6 @cite_8
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In the framework of our research on biologically inspired microrobotics, we have developed a visually based autopilot for micro air vehicles (MAV), which we have called OCTAVE (optical altitude control system for autonomous vehicles). Here, we show the feasibility of a joint altitude and speed control system based on a low complexity optronic velocity sensor that estimates the optic flow in the downward direction. This velocity sensor draws on electrophysiological findings of on the fly elementary motion detectors (EMDs) obtained at our laboratory. We built an elementary, 100-gram tethered helicopter system that carries out terrain following above a randomly textured ground. The overall processing system is light enough to be mounted on-board MAVs with an avionic payload of only some grams. This study describes the design and implementation of several bioinspired algorithms for providing guidance to an ultra-lightweight micro-aerial vehicle (MAV) using a 2.6 g omnidirectional vision sensor. Using this visual guidance system we demonstrate autonomous speed control, centring, and heading stabilisation on board a 30 g MAV flying in a corridorlike environment. In addition to the computation of wide-field optic flow, the comparatively high-resolution omnidirectional imagery provided by this sensor also offers the potential for image-based algorithms such as landmark recognition to be implemented in the future. This paper applies an existing vision-based navigation algorithm to a micro aerial vehicle (MAV). The algorithm has previously been used for long-range navigation of ground robots based on on-board 3D vision sensors such as a stereo or Kinect cameras. A teach-and-repeat operational strategy enables a robot to autonomously repeat a manually taught route without relying on an external positioning system such as GPS. For MAVs we show that a monocular downward looking camera combined with an altitude sensor can be used as 3D vision sensor replacing other resource-expensive 3D vision solutions. The paper also includes a simple path tracking controller that uses feedback from the visual and inertial sensors to guide the vehicle along a straight and level path. Preliminary experimental results demonstrate reliable, accurate and fully autonomous flight of an 8-m-long (straight and level) route, which was taught with the quadrocopter fixed to a cart. Finally, we present the successful flight of a more complex, 16-m-long route. This paper presents a Miniature Aerial Vehicle (MAV) capable of hands- off autonomous operation within indoor environments. Our prototype is a Quadrotor weighing approximately 600g, with a diameter of 550mm, which carries the necessary electronics for stability control, altitude control, collision avoidance and anti-drift control. This MAV is equipped with three rate gyroscopes, three accelerometers, one ultrasonic sensor, four infrared sensors, a high-speed motor controller and a flight computer. Autonomous flight tests have been carried out in a 7x6-m room.