1,458 research outputs found
Obstacle avoidance and distance measurement for unmanned aerial vehicles using monocular vision
Unmanned Aerial Vehicles or commonly known as drones are better suited for "dull, dirty, or dangerous" missions than manned aircraft. The drone can be either remotely controlled or it can travel as per predefined path using complex automation algorithm built during its development. In general, Unmanned Aerial Vehicle (UAV) is the combination of Drone in the air and control system on the ground. Design of an UAV means integrating hardware, software, sensors, actuators, communication systems and payloads into a single unit for the application involved. To make it completely autonomous, the most challenging problem faced by UAVs is obstacle avoidance. In this paper, a novel method to detect frontal obstacles using monocular camera is proposed. Computer Vision algorithms like Scale Invariant Feature Transform (SIFT) and Speeded Up Robust Feature (SURF) are used to detect frontal obstacles and then distance of the obstacle from camera is calculated. To meet the defined objectives, designed system is tested with self-developed videos which are captured by DJI Phantom 4 pro
Visual 3-D SLAM from UAVs
The aim of the paper is to present, test and discuss the implementation of Visual SLAM techniques to images taken from Unmanned Aerial Vehicles (UAVs) outdoors, in partially structured environments. Every issue of the whole process is discussed in order to obtain more accurate localization and mapping from UAVs flights. Firstly, the issues related to the visual features of objects in the scene, their distance to the UAV, and the related image acquisition system and their calibration are evaluated for improving the whole process. Other important, considered issues are related to the image processing techniques, such as interest point detection, the matching procedure and the scaling factor. The whole system has been tested using the COLIBRI mini UAV in partially structured environments. The results that have been obtained for localization, tested against the GPS information of the flights, show that Visual SLAM delivers reliable localization and mapping that makes it suitable for some outdoors applications when flying UAVs
Small Fixed-wing Aerial Positioning Using Inter-vehicle Ranging Combined with Visual Odometry
There has been increasing interest in developing the ability for small unmanned aerial systems (SUAS) to be able to operate in environments where GPS is not available. This research considers the case of a larger aircraft loitering above a smaller GPS-denied SUAS. This larger aircraft is assumed to have greater resources which can overcome the GPS jamming and provide range information to the SUAS flying a mission below. This research demonstrates that using a ranging update combined with an aircraft motion model and visual odometry can greatly improve the accuracy of a SUASs estimated position in a GPS-denied environment
Monocular Visual Odometry with Unmanned Underwater Vehicle Using Low Cost Sensors
The positioning in underwater environments is today a strong necessity for many purposes, such as construction, communication, localization, and environmental monitoring. The use of underwater rover allows to perform visual inspections, maintenance and repair of many infrastructures, like dams, pipes, tunnels, structures as well as the analyses of the underwater environments in lakes, rivers and seas. This work deals with interesting results about monocular visual odometry for unmanned underwater navigation systems. Interesting results have been obtained considering low-cost sensors simulating the real-time, challenging operational conditions, applied for a dedicated archaeological situation. Particular algorithms for navigation procedures and outliers' rejections have been written and will presented in this paper: these aspects have a great importance especially for autonomous navigation solutions in underwater complex environments, such as for archaeological applications
Vision-based localization methods under GPS-denied conditions
This paper reviews vision-based localization methods in GPS-denied
environments and classifies the mainstream methods into Relative Vision
Localization (RVL) and Absolute Vision Localization (AVL). For RVL, we discuss
the broad application of optical flow in feature extraction-based Visual
Odometry (VO) solutions and introduce advanced optical flow estimation methods.
For AVL, we review recent advances in Visual Simultaneous Localization and
Mapping (VSLAM) techniques, from optimization-based methods to Extended Kalman
Filter (EKF) based methods. We also introduce the application of offline map
registration and lane vision detection schemes to achieve Absolute Visual
Localization. This paper compares the performance and applications of
mainstream methods for visual localization and provides suggestions for future
studies.Comment: 32 pages, 15 figure
Precision Navigation Using Pre-Georegistered Map Data
Navigation performance in small unmanned aerial vehicles (UAVs) is adversely affected by limitations in current sensor technology for small, lightweight sensors. Because most UAVs are equipped with cameras for mission-related purposes, it is advantageous to utilize the camera to improve the navigation solution. This research improves navigation by matching camera images to a priori georegistered image data and combining this update with existing image-aided navigation technology. The georegistration matching is done by projecting the images into the same plane, extracting features using the techniques Scale Invariant Feature Transform (SIFT) [5] and Speeded-Up Robust Features (SURF) [3]. The features are matched using the Random Scale and Consensus (RANSAC) [4] algorithm, which generates a model to transform feature locations from one image to another. In addition to matching the image taken by the UAV to the stored images, the effect of matching the images after transforming one to the perspective of the other is investigated. One of the chief advantages of this method is the ability to provide both an absolute position and attitude update. Test results using 15 minutes of aerial video footage at altitudes ranging from 1000m to 1500m demonstrated that transforming the image data from one perspective to the other yields an improvement in performance. The best system configuration uses SIFT on an image that was transformed into the satellite perspective and matched to satellite map data. This process is able to achieve attitude errors on the order of milliradians, and position errors on the order of a few meters vertically. The along track, cross track, and heading errors are higher than expected. Further work is needed on reliability. Once this is accomplished, it should improve the navigation solution of an aircraft, or even provide navigation grade position and attitude estimates in a GPS denied environment
DECENTRALIZED ROBUST NONLINEAR MODEL PREDICTIVE CONTROLLER FOR UNMANNED AERIAL SYSTEMS
The nonlinear and unsteady nature of aircraft aerodynamics together with limited practical range of controls and state variables make the use of the linear control theory inadequate especially in the presence of external disturbances, such as wind. In the classical approach, aircraft are controlled by multiple inner and outer loops, designed separately and sequentially. For unmanned aerial systems in particular, control technology must evolve to a point where autonomy is extended to the entire mission flight envelope. This requires advanced controllers that have sufficient robustness, track complex trajectories, and use all the vehicles control capabilities at higher levels of accuracy. In this work, a robust nonlinear model predictive controller is designed to command and control an unmanned aerial system to track complex tight trajectories in the presence of internal and external perturbance. The Flight System developed in this work achieves the above performance by using: 1 A nonlinear guidance algorithm that enables the vehicle to follow an arbitrary trajectory shaped by moving points; 2 A formulation that embeds the guidance logic and trajectory information in the aircraft model, avoiding cross coupling and control degradation; 3 An artificial neural network, designed to adaptively estimate and provide aerodynamic and propulsive forces in real-time; and 4 A mixed sensitivity approach that enhances the robustness for a nonlinear model predictive controller overcoming the effect of un-modeled dynamics, external disturbances such as wind, and measurement additive perturbations, such as noise and biases. These elements have been integrated and tested in simulation and with previously stored flight test data and shown to be feasible
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