Along with the rise of the smart city movement, Internet of Things is an upcoming phenomenon. Objects and devices are becoming more and more wirelessly interconnected, communicating information between themselves and to human beings. As an extension on static sensor networks that gather real-time environmental data, the feasibility of implementing a dynamic sensor network based on LoRa communication is researched. To achieve such a dynamic system, a self-developed sensor platform was constructed, based on the microcontroller LoPy. Sensors attached to it include a hygrometer, thermometer and microphone. The emphasis of the research was on localisation of the sensors, to put the gathered sensor data into geographical context. A WiFi fingerprinting radiomap was constructed based on available MAC-addresses, their signal strengths, and GPS coordinates. The GPS module was only used for composing the radiomap. When the radiomap is completed, the module can be switched off, only to be switched on for periodical updates of the radiomap. The quality of the radiomap methodology was evaluated by constructing it of measurements gathered in four days, and testing it for the remaining three days. This test gave a correctness of 50% while another 38% of measurements were localised in a neighbouring cell. The correctness can be improved by having a longer training period. The quality of the collected sensor data turned out to be dependent on the weather conditions and the placement location on the carrier vehicle. Vehicle requirements were specified as driving through the city centre and having a schedule and route producing as little noise, heat and air pollution as possible. Another topic of research was LoRa communication, which was deemed as very limited for dynamic implementations, as the sending of location-related data takes up a large part of the already limited message size. To decrypt the sent message and store it in a meaningful database, Node-RED was used. Despite visualisation of measurements showed promising results, there is margin for improvement as far as data capturing is concerned.

Because of urbanisation, more than 50% of the global population now lives in cities, a number that is ever-increasing. This leads to a need for more dense and complex building constructions, driven by the fact that people are living increasingly close to one another. These structures are often so large that it becomes difficult to navigate in the environment without any assistance. Especially for people with restricted mobility, such as wheelchair users, or the blind or partially sighted, it can pose a challenge to find the best path in a building. Consequently there is a need for navigation graphs that provide a way to structure connectivity information and routes in an indoor environment. Existing methods generally obtain information on the location of various rooms, corridors and their interconnectivity from 2D floor plans or 3D building models. However, these plans and models often were created by the architect when the building was planned, but never updated with new information after the building was built and came into use. Manually updating them is very labour-intensive. Therefore, in this research a method is developed for obtaining a navigation graph from an indoor environment by automatically extracting the information needed from a point cloud. The navigation graph is modelled according to the Open Geospatial Consortium (OGC) standard IndoorGML, which provides a basic structure for indoor navigation. The point clouds used in this research are gathered with a hand-held Mobile Laser Scanner (MLS), which also saves the path (trajectory) taken in the building. This all leads to the main research question: How can a navigation network in IndoorGML format automatically be extracted from a cluttered indoor point cloud and its trajectory? In order to answer this question this research focuses on the detection of doorways, and how they connect indoor spaces, such as rooms and corridors. A new way of door detection is proposed, which is based on the identification of walls using the 3D Medial Axis Transform (MAT) of the point cloud. After this it is explored how the established connectivity relationships can be extended with accessibility information, such as the location of stairs, and the dimensions of doors. Additionally it is researched how a more detailed navigation graph can be created by subdividing large spaces that have multiple connections. In this thesis it is proven that geometric input for a navigation graph can automatically be obtained from a point cloud. 100% of doors could be detected in the dataset that the methods were developed on, and when testing the methods on another point cloud, this number decreased to 70% , due to unforeseen situations. All spaces connected by detected doors, stairways and sloped surfaces were included in the graph, from which a more extensive navigation graph could be produced by subdividing corridors. This final product was compared to networks drawn by humans on a corresponding floor plan, from which it can be concluded that the graph produced by the methodology of this thesis is a good basis for a navigation.

In 2014 a team of researchers from five European universities reported on a high tilt susceptibility of the Scintrex CG-5 Autograv land gravity meter. In a series of experiments they demonstrated that the instrument provides incorrect readings after being tilted by angles of at least about 6∘ for a period of at least a few minutes. The readings may be offset by tens of μGal, and it may take hours before the first reliable readings can be taken. They recommend to keep the instrument in upright position within less than the critical angle of about 6∘ during transits, which may be unrealistic during field operations in hilly terrain, during car transportation or when walking with the instrument in a backpack. The instruments tested in 2014 were purchased between 2003 and 2011. Here, we report about the results of a series of experiments with the latest release of the Scintrex CG-5 purchased in 2015 using the same experimental set-up as in 2014. We show that the instrument is still susceptible to tilting though the initial offset has been reduced by about 50%. However, readings may still be offset by tens of μGal if the tilt exceeds about 6∘ and lasts for more than 1 min. Moreover, the time it takes the instrument to provide reliable readings in line with the specifications may still take several hours depending on the temporal duration of instrument tilting. From this we conclude that the problem of tilt susceptibility has not been solved yet.@en