En karta är en färskvara som är i ständigt behov av ajourhållning. Ajourhållning görs normalt med traditionella metoder: fotogrammetriska och/eller geodetiska. Men i och med att utvecklingen går framåt har intresset för en ny metod, UAS (Unmanned Aerial Systems), ökat. UAS är en relativt ny fotogrammetrisk metod där obemannade flygfarkoster används. Detta examensarbete har utvärderat vilken osäkerhet vanligt förekommande detaljer i en karta kan få i framställda "produkter" som genererats med hjälp av UAS-bilder som georefererats med naturliga stödpunkter. Produkterna som framställdes var en digital ytmodell och ett ortofoto och togs fram i datorprogrammet Agisoft Photoscan. Bilderna som bearbetades i denna studie erhölls från Swecos UAS-flygning och var tagna över deponiområdet Fågelmyra i Ornäs, Dalarnas län. I den digitala ytmodellen och i ortofotot mättes detaljer in för att sedan kontrolleras mot kontrollpunkter inmätta med nätverks-RTK (Real-Time Kinematic). Studien visade att detaljer inmätta i den digitala ytmodellen och ortofotot resulterade i en osäkerhet på 0,28 m respektive 0,08 m i plan. Varför osäkerheterna skiljer sig mellan den digitala ytmodellen och ortofotot kan ha att göra med att det är svårt att identifiera objekt i den digitala ytmodellen. Utifrån denna studie kan det konstateras att UAS och georeferering med naturliga stödpunkter lämpar sig för kartering av mindre områden. Dessutom kan det konstateras att UAS är effektiv och relativt enkel teknik. / A map is in constant need of being updated. Map updating is normally performed with traditional methods such as photogrammetric and/or geodetic. But by technical development the interest in new methods has increased, like in UAS (Unmanned Aerial Systems). UAS is a relatively new photogrammetric method using unmanned aerial vehicles (UAV). The purpose of this study is to evaluate what uncertainty common details in a map can get in "products" generated from UAS images georeferenced with natural ground control points. The products that were generated was a digital surface model and an orthophoto and was produced in the software Agisoft Photoscan. The images that were processed in this study were obtained from Sweco’s UAS flight and taken over landfill area Fågelmyra in Ornäs, Dalarna county. In the digital surface model and the orthophoto details were measured and controlled against check points surveyed with Network RTK (Real-Time Kinematic). The study has shown that the surveyed details in the digital surface model and ortohophoto resulted in a planimetric uncertainty of 0.28 m and 0.08 m, respectively. The reason for why the uncertainties for the digital surface model and orthophoto are different may be that it is difficult to identify objects in the digital surface model. Based on this study it can be concluded that UAS and georeferencing with natural ground control points is suitable for mapping of smaller areas. In addition, it can be concluded that UAS is efficient and relatively easy technique.

You do not want your drone to crash. Therefore, safety systems should be put in place to prevent such an event, and obstacle avoidance is a major part of this. Today, the most successful techniques use cameras or light detection and ranging (LIDAR) to find and avoid obstacles; but to improve resiliency, multiple systems should be used. This thesis proposes to use microphones, listening to the drone’s own noise, to estimate the distance to surrounding obstacles. An obstacle echolocation solution for multi-rotor aerial vehicles (MAVs) using ego-noise is developed. The MAV’s noise is captured and auto-correlated to detect echoes at different time delays. This signal is whitened to remove structured measurement noise resulting from the narrow-band components of the MAV’s noise. By recording the MAV’s noise using multiple microphones, a time of arrival (TOA) estimate of the obstacle position is achieved. A beamforming-based solution is used to calculate this estimate. A series of simplified proof-of-concept experiments show that ego-noise echolocation is possible and that the developed solution works in a controlled environment. A prototype implementation of a realistic system is also created. Four signal fusion alternatives are compared, though no best alternative is found for all situations. More work is needed to apply the findings of this work in a robust way, but the principle is shown to work.