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.
In related research, several works have achieved optical flow based control of a MAV , e.g., @cite_23 @cite_19 @cite_13 . As mentioned in the introduction, the standard optical flow methods are computationally too heavy to run on a quadcopter of less than 50 g. For instance, have flown with a 25 g quadcopter before, while computing optical flow for visual odometry @cite_2 . However, this was done on an external computer. As miniaturization of hardware also poses a limitation on communication bandwidth, this can result in a significant delay in the controls. To obtain full autonomy, it would be wise to uncouple a MAV of any external dependencies.
@cite_19 @cite_13 @cite_23 @cite_2
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The problem considered in this paper involves the design of a vision-based autopilot for small and micro Unmanned Aerial Vehicles (UAVs). The proposed autopilot is based on an optic flow-based vision system for autonomous localization and scene mapping, and a nonlinear control system for flight control and guidance. This paper focusses on the development of a real-time 3D vision algorithm for estimating optic flow, aircraft self-motion and depth map, using a low-resolution onboard camera and a low-cost Inertial Measurement Unit (IMU). Our implementation is based on 3 Nested Kalman Filters (3NKF) and results in an efficient and robust estimation process. The vision and control algorithms have been implemented on a quadrotor UAV, and demonstrated in real-time flight tests. Experimental results show that the proposed vision-based autopilot enabled a small rotorcraft to achieve fully-autonomous flight using information extracted from optic flow. In this paper, we present our latest achievements towards the goal of autonomous flights of an MAV in unknown environments, only having a monocular camera as exteroceptive sensor. As MAVs are highly agile, it is not sufficient to directly use the visual input for position control at the framerates that can be achieved with small onboard computers. Our contributions in this work are twofold. First, we present a solution to overcome the issue of having a low frequent onboard visual pose update versus the high agility of an MAV. This is solved by filtering visual information with inputs from inertial sensors. Second, as our system is based on monocular vision, we present a solution to estimate the metric visual scale aid of an air pressure sensor. All computation is running onboard and is tightly integrated on the MAV to avoid jitter and latencies. This framework enables stable flights indoors and outdoors even under windy conditions. 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. 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.