No description available.

A Ground Penetrating Radar (GPR) is a non-invasive measurement tool to locate objects in the subsurface. The GPR transmits electromagnetic waves into the ground and records the waves reflected from surface interfaces of different materials. To accurately find these surfaces after measuring, it is important to record the precise location of the GPR and minimize reflected noise. Since a GPR cannot distinguish the direction from which the waves were reflected, this can result in a misinterpretation of the data if waves are reflected from surrounding objects. This problem can be reduced by also mapping objects in the surroundings. The work of this thesis is aimed at implementing a system that uses a Real-Time Kinematics (RTK) GNSS (Global Navigation Satellite System) receiver for precise positioning together with a 2D-LiDAR (Light Detection And Ranging) to record a 3D map of the surroundings. We used the 3D-LiDAR system to record vertical planes (cross-sections) that were processed into a 3D volume map. We found that the RTK GNSS receiver performed well and delivered the position within centimeters when provided with corrections, while it was about 2.5 m off without corrections. The performance was compared with a professional-grade Leica RTK receiver and the difference in latitude and longitude ranged from 0.001-0.002 m and 0.002-0.004 m, respectively. By fusing the RTK position with the LiDAR data using the software Robot Operating System (ROS), we created 3D maps that represented the surroundings along the traveled path. Our developed system, consisting of an RTK GNSS receiver and the 2D LiDAR, gave promising results and we are optimistic that combining the system with a GPR can improve the interpretation of the subsurface. Thus, the proposed method seems promising to be used during GPR mapping.

Ground subsidence is today very common. It can occur due to man-made or natural causes. Today, it is most common that subsidence occurs in urban areas, and there the causes are almost exclusively man-made, including groundwater extraction, construction under or above ground. This can lead to damage of buildings or other constructions and lead to large costs for reparation. To avoid this, subsidence must be detected in an early stage. Therefore, this thesis work will study the most effective way to detect and measure subsidence using dInSAR, GNSS and levelling. The thesis work will contain a literature study, a compilation of cases comparing the methods and a quantitative comparison of data, called case Stockholm. The main focus of case Stockholm is to compare the RMS error for the datasets and to determine how well the linear regression of the datasets cohere. The literature study and the case compilation presents the strengths and weaknesses of the different methods, where dInSAR’s strength is the ability to measure large areas at once while the weakness is the inability to detect small movements within a large movement. The possibility to measure individual points of interest is the strength of both GNSS and levelling, where the most time-consuming method, levelling, also has the highest accuracy. In case Stockholm, the linear regression for dInSAR mostly follows the linear regression for GNSS and levelling. However, irregular levelling measurements that do not follow the general ground subsidence is missed by dInSAR and the amplitude of the dInSAR measurements differ from both GNSS and levelling measurements. This confirms the strengths and weaknesses mentioned in the literature study. The conclusion that can be drawn from this is that the most effective way of using dInSAR, GNSS and levelling is to first screen large areas for any movement using dInSAR. Later only the areas that display movement of any sort is measured with either GNSS or levelling depending on demands on accuracy. / Sättningar i mark är något som idag är väldigt vanligt. De sker antingen av naturliga orsaker eller skapas av människans påverkan på marken. Idag är den vanligaste förekomsten av sättningar i eller i närheten av stora städer där upptagning av grundvatten samt konstruktion ovan och under mark påverkar marken i så stor utsträckning att skador på byggnader och konstruktioner kan uppstå. För att undvika stora reparationskostnader så är det nödvändigt att upptäcka sättningarna i ett så tidigt skede som möjligt. Denna studie kommer därför behandla möjligheten att på ett så effektivt sätt som möjligt upptäcka och mäta sättningar med hjälp av dInSAR, GNSS och avvägning. Detta kommer göras genom en litteraturstudie, en sammanställning av andra praktiska fall där de tidigare nämnda metoderna jämförts med varandra samt en kvantitativ jämförelse av data över Stockholm. I den kvantitativa jämförelsen kommer vikten ligga på att jämföra metoderna baserat på RMS fel samt hur väl de linjära approximationerna följer varandra för de olika metoderna. Litteraturstudien tillsammans med sammanställningen av de praktiska fallen ger en bred bild av metodernas styrkor och svagheter, där dInSARs styrka ligger i förmågan att läsa av stora områden men dess svaghet är att små individuella rörelser inom en stor rörelse inte kan fångas upp. Styrkan för GNSS och avvägning är punktinmätning, där avvägning har den högsta noggrannheten, men också är mest tidskrävande. Den kvantitativa jämförelsen av data bekräftar styrkorna och svagheterna för metoderna då det var tydligt att dInSAR till största del fångar upp samma markrörelse som både GNSS och avvägning. Det som skiljer dInSAR från GNSS och avvägning är amplituden av mätningarna samt det faktum att vissa avvägda mätningar som visar en annan rörelse än den generella inte fångas upp av dInSAR. Slutsatsen som kan dras från detta är att det mest effektiva sätt att upptäcka och mäta sättningar är att till en början grovt granska stora områden för eventuell rörelse med användning av dInSAR och att sedan, där rörelse uppmäts, göra noggrannare punktmätningar med antingen GNSS eller avvägning, beroende på önskad noggrannhet.

Classical GNSS based positioning has accuracy limitations due to many sources oferror. The error sources range from clock errors and orbit errors toerrors due to variations in atmospheric propagation delays. One way to improve GNSSpositioning is to generate real time corrections using a GNSS reference network.The corrections can then be distributed through the mobilenetwork and be delivered in real time to the device that should position itself.  This thesis aims to develop a positioning engine utilizing statespace representation corrections (SSR). The thesis also has the goal to develop methods for combiningpseudorange measurements with carrier-phase measurements, in the case when SSR correctionsare used. The static and dynamic performance ofthe positioning engine will be evaluated. Also, the  SSR correction format itself, willalso be evaluated and different levels of SSR corrections will be compared. The proposed combined positioning engine uses SSR correctionsand single-difference measurements. Through this, all majorerror sources on the satellite side, device side and in the atmosphere, are removedexcept for an integer ambiguity in the carrier phase measurement. This ambiguityis handled by tracking the GNSS receiver's position along with the integerambiguities in an extended Kalman filter (EKF). Experiments show that usingreal-time SSR corrections leads to a significant improvement in global absolutepositioning for simple GNSS receivers using only a single measurement frequencyand only using pseudorange measurements. For a more advanced receiver capable ofcarrier phase measurements, experiments together with simulation resultsshow that using the proposed combined positioning engine, improves the positioningperformance even further.

No description available.

No description available.

No description available.

No description available.

No description available.

No description available.