Unmanned Aerial Vehicle Vision Research Articles

Vision and Control for UAVs: A Survey of General Methods and of Inexpensive Platforms for Infrastructure Inspection.

Unmanned aerial vehicles (UAVs) have gained significant attention in recent years. Low-cost platforms using inexpensive sensor payloads have been shown to provide satisfactory flight and navigation capabilities. In this report, we survey vision and control methods that can be applied to low-cost UAVs, and we list some popular inexpensive platforms and application fields where they are useful. We also highlight the sensor suites used where this information is available. We overview, among others, feature detection and tracking, optical flow and visual servoing, low-level stabilization and high-level planning methods. We then list popular low-cost UAVs, selecting mainly quadrotors. We discuss applications, restricting our focus to the field of infrastructure inspection. Finally, as an example, we formulate two use-cases for railway inspection, a less explored application field, and illustrate the usage of the vision and control techniques reviewed by selecting appropriate ones to tackle these use-cases. To select vision methods, we run a thorough set of experimental evaluations.

Ensuring the Accuracy of Traffic Monitoring Using Unmanned Aerial Vehicles Vision Systems

The paper is dedicated to the organization of traffic monitoring using unmanned aerial vehicles (UAV). It is demonstrated that the monitoring of traffic makes high demands on the accuracy of determining the position of the vehicle on the road. It is assumed that the purpose of monitoring is detecting specific situations, which may include accident, in particular, car collision; traffic accidents, reducing the bandwidth of the road section; movement of the vehicle, being a threat to other road users. Detection of such situations requires assessment of the following at the received images: Vehicles position relatively to the road markings;Vehicles position relatively to each other;Vehicles speed. Review of the literature showed that the existing tools for tracking ground objects movements provide sufficiently accurate assessment of the vehicles coordinates at the images. Thus, an important issue is the estimation of vehicle position with respect to the road, i.e. in the ground coordinate system of the road. Different options of the vehicle position assessment relatively the road are researched. Evaluation of the content and accuracy of the standard UAV navigation system showed that the option of monitoring based on the use of UAV position assessment relative to the ground coordinate system and the vehicles is non-implementable because of lack of precision at the standard navigation system, including, corrected using the satellite navigation system. Assessing the position of the vehicle relative to the roadside is proposed to be made using image processing algorithms, particularly the contour lines highlighting and the Hough algorithm for straight segments highlighting. The research shows that this option based on direct assessment of the situation with respect to the vehicle position on the road image is physically implementable.

A Cognitive Task Analysis to Elicit Preliminary Requirements for an Automated UAV Verification & Planning System

A future vision for Unmanned Aerial Vehicles (UAVs) includes single operators managing multiple UAVs. Enabling operator management of multiple UAVs may be aided by automated planning systems that incorporate the strategies and constraints preferred by operators. To derive an understanding of these constraints, a table-top exercise was developed and a cognitive task analysis (CTA) was conducted with subject matter experts (SMEs) operating four UAVs in a dynamic mission scenario. Results indicated general consensus among SMEs on high level goals, prioritization of tasks, allocation of UAVs to tasks, and UAV flight paths and sensor parameters for fulfilling each task. These results will be directly incorporated into an automated verification and planning system. Participants also indicated preferred ways the automation would be most helpful, such as tracking and displaying mission details, and generating multiple, feasible plans that can be reviewed and modified before executing, echoing previous studies.

Vision Based UAV Attitude Estimation: Progress and Insights

Unmanned aerial vehicles (UAVs) are increasingly replacing manned systems in situations that are dangerous, remote, or difficult for manned aircraft to access. Its control tasks are empowered by computer vision technology. Visual sensors are robustly used for stabilization as primary or at least secondary sensors. Hence, UAV stabilization by attitude estimation from visual sensors is a very active research area. Vision based techniques are proving their effectiveness and robustness in handling this problem. In this work a comprehensive review of UAV vision based attitude estimation approaches is covered, starting from horizon based methods and passing by vanishing points, optical flow, and stereoscopic based techniques. A novel segmentation approach for UAV attitude estimation based on polarization is proposed. Our future insightes for attitude estimation from uncalibrated catadioptric sensors are also discussed.

Augmenting UAV autonomy

The addition of a camera to an unmanned aerial vehicle (UAV) allows the vehicle to perform a variety of tasks autonomously. This paper presents UAV vision systems that were developed and tested at the Georgia Institute of Technology to perform three of these tasks on the GTMax unmanned research helicopter. The first system demonstrates a vision-based navigation system. The second system demonstrates an automated search routine for stationary ground targets. The third system demonstrates a ground target tracking architecture for mobile targets