Structure from motion (SFM) is a methodology for automatically reconstructing three-dimensional (3D) models from a series of two-dimensional (2D) images when there is no a priori knowledge of the camera location and direction. Modern unmanned aerial vehicles (UAV) now provide a low-cost means of obtaining aerial video footage of a point of interest. Unfortunately, raw video lacks the required information for SFM software, as it does not record exchangeable image file (EXIF) information for the frames. In this work, a solution is presented to modify aerial video so that it can be used for photogrammetry. The paper then examines how the field of view effects the quality of the reconstruction. The input is unstabilized, and distorted video footage obtained from a low-cost UAV which is then combined with an open-source SFM system to reconstruct a 3D model. This approach creates a high quality reconstruction by reducing the amount of unknown variables, such as focal length and sensor size, while increasing the data density. The experiments conducted examine the optical field of view settings to provide sufficient overlap without sacrificing image quality or exacerbating distortion. The system costs less than e1000, and the results show the ability to reproduce 3D models that are of centimeter-level accuracy. For verification, the results were compared against millimeter-level accurate models derived from laser scanning.

This paper compares several feature detectors applied to imagery from an unmanned aerial vehicle to find the best detection algorithm when applied to datasets that vary in translation and have little or no image overlap. Metrics of inliers and reconstruction accuracy of feature detectors are considered with respect to three-dimensional reconstruction results. The image matching results are tested experimentally, and an approach to detecting false matches is outlined. Results showed that although the detectors varied in the number of keypoints generated, a large number of inliers does not necessarily translate into more points in the final point cloud reconstruction and that the process of comparing a large quantity of redundant keypoints may outweigh the advantage of having the extra points. The results also showed that despite the development of keypoint detectors and descriptors, none of them consistently demonstrated a substantial improvement in the quality of structure from motion reconstruction when applied to a wide range of disparate urban and rural images.

Robotics is the field currently taking its place as a leading candidate for dramatic changes in everyday life. Advances in the past 10 years in sensing, actuator and power technologies have fuelled an explosion of opportunities in this exciting, and surprisingly affordable domain. Small Unmanned Aircraft Systems (drones) are being rapidly developed for research, public service, and commercial applications, all around the globe. Drones offer a number of unique affordances to mobile technology research for community empowerment and `extreme user' access: they are relatively inexpensive, easy to operate and to fit with alternative interfaces for people of all abilities, and are readily available. Furthermore, they can carry a payload of light, inexpensive, and off-the-shelf sensors that can be used to support a wide range of research efforts. A potential helping application has been developed in UCD for live streaming of visuals for people with limited mobility. The aim is to use drones and virtual reality as surrogates to provide access to visual information to differently-abled people. This paper will summarize the state of the art in drones research in terms of the affordances to assistive technology. Imagine being able to take any sport or imagine any far-away and otherwise inaccessible cultural heritage or educational site and to bring these rich assets to life within an immersive environment. The paper proposes a system to integrate virtual reality (VR) with a low-cost unmanned, semi-autonomous quad rotor. This quad rotor with a VR headset would allow for first-person vision and manipulation using the Robot Operating System. The system would enable the user to move the quad rotor remotely using natural head movements, which could be tracked by the VR headset and translated into six degrees of freedom commands that would then be sent to the quad rotor. This would facilitate operation when compared to commercially available controller methods, as it would immerse the user in the robot's environment. The paper presents the software components that will allow people off site to strap themselves into a virtual reality headset (i.e. Oculus Rift at the top end of technology, or one of the many emerging affordable VR headsets such as Google cardboard) and then - via footage taken from drone flights and other digital media - to be able to participate virtually, in real time, with other users who are all synchronously immersed in these environments. From the point of view of the camera on the plane overhead, the viewer can get a very good impression of a site, seeing things that you would not normally be visible even during a `real' walk of the same terrain. Overtime, the aim is to facilitate people with limited mobility to have access to live streaming of visuals and thus to experience the sense of empowerment and inclusion that live engagement in physical activity can inspire and support.

This paper proposes a flood risk visualization method that is (1) readily transferable (2) hyperlocal, (3) computationally inexpensive, and (4) geometrically accurate. This proposal is for risk communication, to provide high-resolution, three-dimensional flood visualization at the sub-meter level. The method couples a laser scanning point cloud with algorithms that produce textured floodwaters, achieved through compounding multiple sine functions in a graphics shader. This hyper-local approach to visualization is enhanced by the ability to portray changes in (i) watercolor, (ii) texture, and (iii) motion (including dynamic heights) for various flood prediction scenarios. Through decoupling physics-based predictions from the visualization, a dynamic, flood risk viewer was produced with modest processing resources involving only a single, quad-core processor with a frequency around 4.30 GHz and with no graphics card. The system offers several major advantages. (1) The approach enables its use on a browser or with inexpensive, virtual reality hardware and, thus, promotes local dissemination for flood risk communication, planning, and mitigation. (2) The approach can be used for any scenario where water interfaces with the built environment, including inside of pipes. (3) When tested for a coastal inundation scenario from a hurricane, 92% of the neighborhood participants found it to be more effective in communicating flood risk than traditional 2D mapping flood warnings provided by governmental authorities.