Volume 9, Issue 4

Let us have a group of points in 2D given by their coordinates in a system I. The points are located among other points with coordinates given in system II. The coordinates in the system I are much more accurate than the coordinates in the system II. The problem is to find the coordinates in the system II of the points of the given group however in such a way that the Euclidean distance between any two points are of the same accuracy as in the system I.

Interest in using inexpensive Unmanned Aerial System (UAS) technology for topographic mapping has recently significantly increased. Small UAS platforms equipped with consumer grade cameras can easily acquire high-resolution aerial imagery allowing for dense point cloud generation, followed by surface model creation and orthophoto production. In contrast to conventional airborne mapping systems, UAS has limited ground coverage due to low flying height and limited flying time, yet it offers an attractive alternative to high performance airborne systems, as the cost of the sensors and platform, and the flight logistics, is relatively low. In addition, UAS is better suited for small area data acquisitions and to acquire data in difficult to access areas, such as urban canyons or densely built-up environments. The main question with respect to the use of UAS is whether the inexpensive consumer sensors installed in UAS platforms can provide the geospatial data quality comparable to that provided by conventional systems. This study aims at the performance evaluation of the current practice of UAS-based topographic mapping by reviewing the practical aspects of sensor configuration, georeferencing and point cloud generation, including comparisons between sensor types and processing tools. The main objective is to provide accuracy characterization and practical information for selecting and using UAS solutions in general mapping applications. The analysis is based on statistical evaluation as well as visual examination of experimental data acquired by a Bergen octocopter with three different image sensor configurations, including a GoPro HERO3+ Black Edition, a Nikon D800 DSLR and a Velodyne HDL-32. In addition, georeferencing data of varying quality were acquired and evaluated. The optical imagery was processed by using three commercial point cloud generation tools. Comparing point clouds created by active and passive sensors by using different quality sensors, and finally, by different commercial software tools, provides essential information for the performance validation of UAS technology.

In recent years, kinematic laser scanning has become increasingly popular because it offers many benefits compared to static laser scanning. The advantages include both saving of time in the georeferencing and a more favorable scanning geometry. Often mobile laser scanning systems are installed on wheeled platforms, which may not reach all parts of the object. Hence, there is an interest in the development of portable systems, which remain operational even in inaccessible areas. The development of such a portable laser scanning system is presented in this paper. It consists of a lightweight direct georeferencing unit for the position and attitude determination and a small low-cost 2D laser scanner. This setup provides advantages over existing portable systems that employ heavy and expensive 3D laser scanners in a profiling mode. A special emphasis is placed on the system calibration, i. e. the determination of the transformation between the coordinate frames of the direct georeferencing unit and the 2D laser scanner. To this end, a calibration field is used, which consists of differently orientated georeferenced planar surfaces, leading to estimates for the lever arms and boresight angles with an accuracy of mm and one-tenth of a degree. Finally, point clouds of the mobile laser scanning system are compared with georeferenced point clouds of a high-precision 3D laser scanner. Accordingly, the accuracy of the system is in the order of cm to dm. This is in good agreement with the expected accuracy, which has been derived from the error propagation of previously estimated variance components.

PNT stands for Positioning, Navigation, and Timing. Space-based PNT refers to the capabilities enabled by GNSS, and enhanced by Ground and Space-based Augmentation Systems (GBAS and SBAS), which provide position, velocity, and timing information to an unlimited number of users around the world, allowing every user to operate in the same reference system and timing standard. Such information has become increasingly critical to the security, safety, prosperity, and overall qualityof-life of many citizens. As a result, space-based PNT is now widely recognized as an essential element of the global information infrastructure. This paper discusses the importance of the availability and continuity of PNT information, whose application, scope and significance have exploded in the past 10–15 years. A paradigm shift in the navigation solution has been observed in recent years. It has been manifested by an evolution from traditional single sensor-based solutions, to multiple sensor-based solutions and ultimately to collaborative navigation and layered sensing, using non-traditional sensors and techniques – so called signals of opportunity. A joint working group under the auspices of the International Federation of Surveyors (FIG) and the International Association of Geodesy (IAG), entitled ‘Ubiquitous Positioning Systems’ investigated the use of Collaborative Positioning (CP) through several field trials over the past four years. In this paper, the concept of CP is discussed in detail and selected results of these experiments are presented. It is demonstrated here, that CP is a viable solution if a ‘network’ or ‘neighbourhood’ of users is to be positioned / navigated together, as it increases the accuracy, integrity, availability, and continuity of the PNT information for all users.

Location-based Services (LBS) influence nowadays every individual’s life due to the emerging market penetration of smartphones and other mobile devices. For smartphone Apps localization technologies are developed ranging from GNSS beyond to other alternative ubiquitous positioning methods as well as the use of the in-built inertial sensors, such as accelerometers, gyroscopes, magnetometer, barometer, etc. Moreover, signals-of-opportunity which are not intended for positioning at the first sight but are receivable in many environments such as in buildings and public spaces are more and more utilized for positioning and navigation. The use of Wi-Fi (Wireless Fidelity) is a typical example. These technologies, however, have become very powerful tools as the enable to track an individual or even a group of users. Most technical researchers imply that it is mainly about further enhancing technologies and algorithms including the development of new advanced Apps to improve personal navigation and to deliver location oriented information just in time to a single LBS user or group of users. The authors claim that there is a need that ethical and political issues have to be addressed within our research community from the very beginning. Although there is a lot of research going on in developing algorithms to keep ones data and LBS search request in private, researchers can no longer keep their credibility without cooperating with ethical experts or an ethical committee. In a study called InKoPoMoVer (Cooperative Positioning for Real-time User Assistance and Guidance at Multi-modal Public Transit Junctions) a cooperation with social scientists was initiated for the first time at the Vienna University of Technology, Austria, in this context. The major aims of this study in relation to ethical questions are addressed in this paper.