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.
@cite_14: We aim at developing autonomous miniature hovering flying robots capable of navigating in unstructured GPS-denied environments. A major challenge is the miniaturization of the embedded sensors and processors that allow such platforms to fly by themselves. In this paper, we propose a novel ego-motion estimation algorithm for hovering robots equipped with inertial and optic-flow sensors that runs in real-time on a microcontroller and enables autonomous flight. Unlike many vision-based methods, this algorithm does not rely on feature tracking, structure estimation, additional distance sensors or assumptions about the environment. In this method, we introduce the translational optic-flow direction constraint, which uses the optic-flow direction but not its scale to correct for inertial sensor drift during changes of direction. This solution requires comparatively much simpler electronics and sensors and works in environments of any geometry. Here we describe the implementation and performance of the method on a hovering robot equipped with eight 0.65 g optic-flow sensors, and show that it can be used for closed-loop control of various motions.
@cite_4: Flying in and around caves, tunnels, and buildings demands more than one sensing modality. This article presented an optic-flow- based approach inspired by flying insects for avoiding lateral collisions. However, there were a few real-world scenarios in which optic flow sensing failed. This occurred when obstacles on approach were directly in front of the aircraft. Here, a simple sonar or infrared sensor can be used to trigger a quick transition into the hovering mode to avoid the otherwise fatal collision. Toward this end, we have demonstrated a fixed-wing prototype capable of manually transitioning from conventional cruise flight into the hovering mode. The prototype was then equipped with an IMU and a flight control system to automate the hovering process. The next step in this research is to automate the transition from cruise to hover flight.
@cite_6: 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.
@cite_8: We aim at developing autonomous miniature hov- ering flying robots capable of navigating in unstructured GPS- denied environments. A major challenge is the miniaturization of the embedded sensors and processors allowing such platforms to fly autonomously. In this paper, we propose a novel ego-motion estimation algorithm for hovering robots equipped with inertial and optic-flow sensors that runs in real- time on a microcontroller. Unlike many vision-based methods, this algorithm does not rely on feature tracking, structure estimation, additional distance sensors or assumptions about the environment. Key to this method is the introduction of the translational optic-flow direction constraint (TOFDC), which does not use the optic-flow scale, but only its direction to correct for inertial sensor drift during changes of direction. This solution requires comparatively much simpler electronics and sensors and works in environments of any geometries. We demonstrate the implementation of this algorithm on a miniature 46g quadrotor for closed-loop position control.
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.