MusA: Using Indoor Positioning and Navigation to Enhance Cultural Experiences in a museum

In recent years there has been a growing interest into the use of multimedia mobile guides in museum environments. Mobile devices have the capabilities to detect the user context and to provide pieces of information suitable to help visitors discovering and following the logical and emotional connections that develop during the visit. In this scenario, location based services (LBS) currently represent an asset, and the choice of the technology to determine users' position, combined with the definition of methods that can effectively convey information, become key issues in the design process. In this work, we present MusA (Museum Assistant), a general framework for the development of multimedia interactive guides for mobile devices. Its main feature is a vision-based indoor positioning system that allows the provision of several LBS, from way-finding to the contextualized communication of cultural contents, aimed at providing a meaningful exploration of exhibits according to visitors' personal interest and curiosity. Starting from the thorough description of the system architecture, the article presents the implementation of two mobile guides, developed to respectively address adults and children, and discusses the evaluation of the user experience and the visitors' appreciation of these application

Smart environment monitoring through micro unmanned aerial vehicles

In recent years, the improvements of small-scale Unmanned Aerial Vehicles (UAVs) in terms of flight time, automatic control, and remote transmission are promoting the development of a wide range of practical applications. In aerial video surveillance, the monitoring of broad areas still has many challenges due to the achievement of different tasks in real-time, including mosaicking, change detection, and object detection. In this thesis work, a small-scale UAV based vision system to maintain regular surveillance over target areas is proposed. The system works in two modes. The first mode allows to monitor an area of interest by performing several flights. During the first flight, it creates an incremental geo-referenced mosaic of an area of interest and classifies all the known elements (e.g., persons) found on the ground by an improved Faster R-CNN architecture previously trained. In subsequent reconnaissance flights, the system searches for any changes (e.g., disappearance of persons) that may occur in the mosaic by a histogram equalization and RGB-Local Binary Pattern (RGB-LBP) based algorithm. If present, the mosaic is updated. The second mode, allows to perform a real-time classification by using, again, our improved Faster R-CNN model, useful for time-critical operations. Thanks to different design features, the system works in real-time and performs mosaicking and change detection tasks at low-altitude, thus allowing the classification even of small objects. The proposed system was tested by using the whole set of challenging video sequences contained in the UAV Mosaicking and Change Detection (UMCD) dataset and other public datasets. The evaluation of the system by well-known performance metrics has shown remarkable results in terms of mosaic creation and updating, as well as in terms of change detection and object detection

NEW GEOMETRIC MODELING AND WORKFLOW FOR THE AIRBORNE DIGITAL SENSOR (ADS40): A FAST, EFFICIENT, RELIABLE AND HIGHLY ACCURATE APPROACH

ABSTRACT For the past several decades, there has been much interest in advancing photogrammetry, from acquisition to processing, from an analog technology to digital technology and much of this development has focused on using scanned aerial photographs. However, in order to truly bridge the gap between data acquisition and production, there has been a great need to develop digital acquisition capability. For the last few years, there have been a limited number of attempts to design two-dimensional photogrammetric digital cameras however, the limited number of pixels has hindered their applicability to extensive large scale mapping applications. Linear scanning technology can overcome such a limitation by increasing the number of pixels across the flying direction. Ground coverage along the flying direction is achieved by capturing adjacent image strips while advancing the airborne platform. The Airborne Digital Sensor ADS40 by Leica Geosystems is one such sensor that has been used for production since it was first released in 2000. It incorporates fully integrated GPS and IMU sensors that are used to observe the position and attitude of the camera in an integration process. However, the observations from the GPS and IMU sensors may be contaminated with bias values which will degrade the final adjustment results. In this paper, a new model for the ADS40 is developed. This model takes into account different GPS/IMU biases that are contained in the raw observations and/or result from the integration process. Estimating, and therefore correcting for, these bias values is achieved by utilizing a minimum number of ground control points. A new bundle adjustment procedure has been developed at North West Geomatics Ltd. that has been proven to generate highly accurate results. Based on this successful model, a new workflow that is fast, efficient and reliable has been adopted

Using Linear Features for Aerial Image Sequence Mosaiking

With recent advances in sensor technology and digital image processing techniques, automatic image mosaicking has received increased attention in a variety of geospatial applications, ranging from panorama generation and video surveillance to image based rendering. The geometric transformation used to link images in a mosaic is the subject of image orientation, a fundamental photogrammetric task that represents a major research area in digital image analysis. It involves the determination of the parameters that express the location and pose of a camera at the time it captured an image. In aerial applications the typical parameters comprise two translations (along the x and y coordinates) and one rotation (rotation about the z axis). Orientation typically proceeds by extracting from an image control points, i.e. points with known coordinates. Salient points such as road intersections, and building corners are commonly used to perform this task. However, such points may contain minimal information other than their radiometric uniqueness, and, more importantly, in some areas they may be impossible to obtain (e.g. in rural and arid areas). To overcome this problem we introduce an alternative approach that uses linear features such as roads and rivers for image mosaicking. Such features are identified and matched to their counterparts in overlapping imagery. Our matching approach uses critical points (e.g. breakpoints) of linear features and the information conveyed by them (e.g. local curvature values and distance metrics) to match two such features and orient the images in which they are depicted. In this manner we orient overlapping images by comparing breakpoint representations of complete or partial linear features depicted in them. By considering broader feature metrics (instead of single points) in our matching scheme we aim to eliminate the effect of erroneous point matches in image mosaicking. Our approach does not require prior approximate parameters, which are typically an essential requirement for successful convergence of point matching schemes. Furthermore, we show that large rotation variations about the z-axis may be recovered. With the acquired orientation parameters, image sequences are mosaicked. Experiments with synthetic aerial image sequences are included in this thesis to demonstrate the performance of our approach

The Exploitation of Data from Remote and Human Sensors for Environment Monitoring in the SMAT Project

In this paper, we outline the functionalities of a system that integrates and controls a fleet of Unmanned Aircraft Vehicles (UAVs). UAVs have a set of payload sensors employed for territorial surveillance, whose outputs are stored in the system and analysed by the data exploitation functions at different levels. In particular, we detail the second level data exploitation function whose aim is to improve the sensors data interpretation in the post-mission activities. It is concerned with the mosaicking of the aerial images and the cartography enrichment by human sensors—the social media users. We also describe the software architecture for the development of a mash-up (the integration of information and functionalities coming from the Web) and the possibility of using human sensors in the monitoring of the territory, a field in which, traditionally, the involved sensors were only the hardware ones.JRC.H.6-Digital Earth and Reference Dat

Micro Aerial Vehicles (MAV) Assured Navigation in Search and Rescue Missions Robust Localization, Mapping and Detection

This Master's Thesis describes the developments on robust localization, mapping and detection algorithms for Micro Aerial Vehicles (MAVs). The localization method proposes a seamless indoor-outdoor multi-sensor architecture. This algorithm is capable of using all or a subset of its sensor inputs to determine a platform's position, velocity and attitude (PVA). It relies on the Inertial Measurement Unit as the core sensor and monitors the status and observability of the secondary sensors to select the most optimum estimator strategy for each situation. Furthermore, it ensures a smooth transition between filters structures. This document also describes the integration mechanism for a set of common sensors such as GNSS receivers, laser scanners and stereo and mono cameras. The mapping algorithm provides a fully automated fast aerial mapping pipeline. It speeds up the process by pre-selecting the images using the flight plan and the onboard localization. Furthermore, it relies on Structure from Motion (SfM) techniques to produce an optimized 3D reconstruction of camera locations and sparse scene geometry. These outputs are used to compute the perspective transformations that project the raw images on the ground and produce a geo-referenced map. Finally, these maps are fused with other domains in a collaborative UGV and UAV mapping algorithms. The real-time aerial detection of victims is based on a thermal camera. The algorithm is composed by three steps. Firstly, a normalization of the image is performed to get rid of the background and to extract the regions of interest. Later the victim detection and tracking steps produce the real-time geo-referenced locations of the detections. The thesis also proposes the concept of a MAV Copilot, a payload composed by a set of sensors and algorithm the enhances the capabilities of any commercial MAV. To develop and validate these contributions, a prototype of a search and rescue MAV and the Copilot has been developed. These developments have been validated in three large-scale demonstrations of search and rescue operations in the context of the European project ICARUS: a shipwreck in Lisbon (Portugal), an earthquake in Marche (Belgium), and the Fukushima nuclear disaster in the euRathlon 2015 competition in Piombino (Italy)

USE OF UNMANNED AERIAL VEHICLES (UAV) FOR URBAN TREE INVENTORIES

In contrast to standard aerial imagery, unmanned aerial systems (UAS) utilize recent technological advances to provide an affordable alternative for imagery acquisition. Increased value can be realized through clarity and detail providing higher resolution (2-5 cm) over traditional products. Many natural resource disciplines such as urban forestry will benefit from UAS. Tree inventories for risk assessment, biodiversity, planning, and design can be efficiently achieved with the UAS. Recent advances in photogrammetric processing have proved automated methods for three dimensional rendering of aerial imagery. Point clouds can be generated from images providing additional benefits. Association of spatial locational information within the point cloud can be used to produce elevation models i.e. digital elevation, digital terrain and digital surface. Taking advantage of this point cloud data, additional information such as tree heights can be obtained. Several software applications have been developed for LiDAR data which can be adapted to utilize UAS point clouds. This study examines solutions to provide tree inventory and heights from UAS imagery. Imagery taken with a micro-UAS was processed to produce a seamless orthorectified image. This image provided an accurate way to obtain a tree inventory within the study boundary. Utilizing several methods, tree height models were developed with variations in spatial accuracy. Model parameters were modified to offset spatial inconsistencies providing statistical equality of means. Statistical results (p = 0.756) with a level of significance (α = 0.01) between measured and modeled tree height means resulted with 82% of tree species obtaining accurate tree heights. Within this study, the UAS has proven to be an efficient tool for urban forestry providing a cost effective and reliable system to obtain remotely sensed data

Automated 3D object modeling from aerial video imagery

Research in physically accurate 3D modeling of a scene is gaining momentum because of its far reaching applications in civilian and defense sectors. The modeled 3D scene must conform both geometrically and spectrally to the real world for all the applications. Geometric modeling of a scene can be achieved in many ways of which the two most popular methods are - a) using multiple 2D passive images of the scene also called as stereo vision and b) using 3D point clouds like Lidar (Light detection and ranging) data. In this research work, we derive the 3D models of objects in a scene using passive aerial video imagery. At present, this geometric modeling requires a lot of manual intervention due to a variety of factors like sensor noise, low contrast conditions during image capture, etc. Hence long time periods, in the order of weeks and months, are required to model even a small scene. This thesis focuses on automating the process of geometric modeling of objects in a scene from passive aerial video imagery. The aerial video frames are stitched into stereo mosaics. These stereo mosaics not only provide the elevation information of a scene but also act as good 3D visualization tools. The 3D information obtained from the stereo mosaics is used to identify the various 3D objects, especially man-made buildings using probabilistic inference provided by Bayesian Networks. The initial 3D building models are further optimized by projecting them on to the individual video frames. The limitations of the state-of-art technology in attaining these goals are presented along with the techniques to overcome them. The improvement that can be achieved in the accuracy of the 3D models when Lidar data is fused with aerial video during the object identification process is also examined