This work addresses the problem of coupling vision-based navigation systems for Unmanned Aerial Vehicles (UAVs) with robust obstacle avoidance capabilities. The former is formulated by a maximization of the point of interest visibility, while the latter is modeled by ellipsoidal repulsive areas. The whole problem is transcribed into an Optimal Control Problem (OCP), and solved in a few milliseconds by leveraging state-of-the-art numerical optimization. The resulting trajectories are then well suited to achieve the specified goal location while avoiding obstacles by a safety margin and minimizing the probability to loose track with the target of interest. Combining this technique with a proper ellipsoid shaping (e.g. augmenting the shape with the obstacle velocity, or the obstacle detection uncertainties) results in a robust obstacle avoidance behaviour. We validate our approach within extensive simulated experiments demonstrating (i) capability to satisfy all the constraints, and (ii) the avoidance reactivity even in challenging situations. We release with this paper the open source implementation
Steering a robot to its desired state by using visual feedbacks obtained from one or more cameras (requirement 3) is formally defined as Visual Servoing (VS), with several applications within the UAV domain @cite_15 @cite_24 @cite_5 @cite_22 . Among the others, in Falanga @cite_22 , the authors address the flight through narrow gaps by proposing an active-vision approach and by relying only on onboard sensing and computing. The system is capable to provide an accurate trajectory while simultaneously estimating the UAV's position by detecting the gap in the camera images. Nevertheless, it might fail in presence of unmodeled obstacles along the path.
@cite_24 @cite_5 @cite_15 @cite_22
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For robotic systems to realise lifelong autonomy they must be able to navigate accurately in changing environments. In this thesis we describe, implement and validate a new approach to the problem of long-term navigation. To begin, we present our stereo visual odometry system which provides highly accurate pose estimation. Our approach combines several techniques found in existing implementations and a recently published image descriptor that simplifies the solution architecture. The performance and versatility of our system is demonstrated through testing on multiple datasets. Equipped with our visual odometry system, we describe a new approach to the problem of lifelong navigation. We learn a model whose complexity varies naturally in accordance with the variation of scene appearance. As the robot repeatedly traverses its workspace, it accumulates distinct visual experiences that, in concert, implicitly represent the scene variation - each experience captures a visual mode. When operating in a previously visited area, we continually try to localise in these previous experiences while simultaneously running the visual odometry. Failure to localise in a sufficient number of prior experiences indicates an insufficient model of the workspace and instigates the laying down of the live image sequence as a new distinct experience. In this way, over time we can capture the typical temporally varying appearance of an environment and the number of experiences required tends to a constant. Although we focus on vision as a primary sensor, the ideas we present here are equally applicable to other sensor modalities. We demonstrate our approach working on a road vehicle operating over a three month period at different times of day, in different weather and lighting conditions. We address one of the main challenges towards autonomous quadrotor flight in complex environments, which is flight through narrow gaps. While previous works relied on off-board localization systems or on accurate prior knowledge of the gap position and orientation in the world reference frame, we rely solely on onboard sensing and computing and estimate the full state by fusing gap detection from a single onboard camera with an IMU. This problem is challenging for two reasons: (i) the quadrotor pose uncertainty with respect to the gap increases quadratically with the distance from the gap; (ii) the quadrotor has to actively control its orientation towards the gap to enable state estimation (i.e., active vision). We solve this problem by generating a trajectory that considers geometric, dynamic, and perception constraints: during the approach maneuver, the quadrotor always faces the gap to allow state estimation, while respecting the vehicle dynamics; during the traverse through the gap, the distance of the quadrotor to the edges of the gap is maximized. Furthermore, we replan the trajectory during its execution to cope with the varying uncertainty of the state estimate. We successfully evaluate and demonstrate the proposed approach in many real experiments, achieving a success rate of 80 and gap orientations up to 45°. To the best of our knowledge, this is the first work that addresses and achieves autonomous, aggressive flight through narrow gaps using only onboard sensing and computing and without prior knowledge of the pose of the gap. 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. In this article, image-based visual servoing control of an underactuated unmanned aerial vehicle is considered for the three-dimensional translational motion. Taking into account the low quality of accelerometers’ data, the main objective of this article is to only use information of rate gyroscopes and a camera, as the sensor suite, in order to design an image-based visual servoing controller. Kinematics and dynamics of the unmanned aerial vehicle are expressed in terms of visual information, which make it possible to design dynamic image-based visual servoing controllers without using linear velocity information obtained from accelerometers. Image features are selected through perspective image moments of a flat target plane in which no geometric information is required, and therefore, the approach can be applied in unknown environments. Two output feedback controllers that deal with uncertainties in dynamics of the system related to the motion of the target and also unknown depth information of the image are proposed using a linear observer. Stability analysis guarantees that the errors of the system remain uniformly ultimately bounded during a tracking mission and converge to 0 when the target is stationary. Simulation results are presented to validate the designed controllers.