Analysis of Positional Precision When Using Ground Control Points with Supported INS in GNSS-Free Environments
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DEGREE PROJECT IN CIVIL ENGINEERING AND URBAN MANAGEMENT, SECOND CYCLE, 30 CREDITS STOCKHOLM, SWEDEN 2021 Analysis of Positional Precision when Using Ground Control Points with Supported INS in GNSS-Free Environments LINUS BÄCKSTRÖM PATRIK GRENERT KTH ROYAL INSTITUTE OF TECHNOLOGY SCHOOL OF ARCHITECTURE AND THE BUILT ENVIRONMENT Acknowledgements Milan Horemuz, KTH Division of Geoinformatics, supervisor. For helping us when we got stuck, always being available to meet and for reading our drafts and giving feedback. Peter Östrand, WSP, supervisor. For giving us concrete ideas of what to research, being open to meet when we needed it and explaining WSPs mobile mapping process for us. Johan Vium Andersson, WSP, supervisor. For explaining the more technical aspects of the geodetic applications and requirements that are important for our work. Martin Brandin, WSP. For helping us with the used data as well as for explaining how to use the softwares that were utilized throughout the thesis. WSP. For offering us the possibility to work on this topic as well as for supplying us with the required data. 1 Title Analysis of Positional Precision when Using Ground Control Points with Supported INS in GNSS-Free Environments Authors Linus Bäckström and Patrik Grenert Department Real Estate and Construction Management TRITA number TRITA-ABE-MBT-21377 Supervisors Milan Horemuz, Johan Vium Andersson and Peter Östrand Keywords Track maintenance, GNSS, INS, GCP, GNSS-free environments, Railway positioning Abstract Railway traffic is one of the most used transportation methods in today's society both for freight transports and transportation of people. A necessity for this to function is that the tracks upon which the trains travel are functional. This includes both that the tracks have been constructed correctly and that the tracks have not experienced wear and tear to the level that their functionality is in jeopardy. This requires that the tracks are thoroughly maintained and thus a continuous knowledge about the state of the tracks is required. One way to obtain knowledge about the current track geometry is to measure the tracks using laser scanners to establish the tracks geographical position. This in turn leads to the possibility to notice changes in the tracks. These laser scanners can be mounted on trains or modified vehicles where they scan the tracks while the vehicle is moving along the tracks. However, the scanned points also have to be precisely located in a coordinate system so that they can be compared to the scanned geometry of the initial tracks. The precise locations can be acquired by using Global Navigation Satellite Systems (GNSS) along with Inertial Measurement Systems (INS) and odometers, which are then used as input in a Kalman filter. The GNSS and INS complement each other in a good way since INS have very high positional accuracy and a large temporal error while GNSS has an acceptable positional accuracy and no temporal error. In locations where there is sufficient GNSS availability, this method reaches positional accuracies around the low cm level. The aforementioned method does however struggle when there is subpar GNSS availability, for example in tunnels or in dense forests. This necessitates the use of additional data, and in this work the use of ground control points (GCP) have been examined. The GCPs have been implemented in simulated GNSS-free areas where a temporal distance of 2, 5, 10, 20 and 40 seconds between GCPs has been used. Based on these experiments, an estimated positional accuracy from 0.5 cm to 30 cm in GNSS-free environments has been acquired depending on the distance between points. The authors recommend an implementation of GCPs in a tightly coupled approach every 5-10 seconds to achieve a reliable positional precision on the mm-cm level. The disadvantages of GCPs are quite large since they have to be established and maintained, which costs a fair amount of money and time. It is therefore of utmost importance to minimize the need for GCPs. This can be accomplished either by using alternative solutions such as implementations of track alignment in the Kalman filter, but also by increasing the efficiency of the GCPs. The way that this thesis recommends this to be researched is to use the same GCPs multiple times by either using more advanced sensors for locating the GCPs or by increasing the number of sensors as well as spreading them out across the vehicle. 2 Titel Undersökning av positionsprecision vid utnyttjande av kända punkter tillsammans med INS i områden utan GNSS Författare Linus Bäckström och Patrik Grenert Institution Fastigheter och Byggande TRITA nummer TRITA-ABE-MBT-21377 Handledare Milan Horemuz, Johan Vium Andersson och Peter Östrand Nyckelord Rälsunderhåll, GNSS, INS, Kända punkter, Avsaknad av GNSS, Positionering på räls Sammanfattning Tågtrafik är ett av de mest använda transportsätten idag vare sig det gäller godstransporter eller persontransporter. En nödvändighet för att detta ska fungera är att rälsen som tågen färdas på är funktionella. Detta inkluderar att rälsen är korrekt konstruerad, men även att rälsen inte har blivit skadade av bland annat kontinuerlig användning. Därmed behöver rälsen underhållas, och för att kunna göra det krävs kunskap om i vilket skick rälsen är. Ett sätt att införskaffa kunskap om rälsens skick är att mäta rälsen med hjälp av laserskanners. Dessa laserskanners kan monteras på tåg eller rälsanpassade fordon så att de kan mäta in rälsen samtidigt som fordonet färdas längs med rälsen. De inmätta punkterna måste emellertid även vara kända i ett koordinatsystem så att de kan jämföras med punkterna som mättes in när rälsen initialt skannades. Den precisa platsinformationen som krävs för detta kan införskaffas genom att använda Global Navigation Satellite Systems (GNSS) samt Inertial Measurement Systems (INS) och odometer, som sedan används som input i ett Kalmanfilter. GNSS och INS kompletterar varandra på ett bra vis eftersom INS har en väldigt hög platsnoggrannhet samt ett högt tidsberoende fel medan GNSS har relativt hög platsnoggrannhet och inget tidsberoende fel. Denna metod kan därmed nå noggrannheter runt cm- nivån när det är bra GNSS-förutsättningar. Metoden som beskrevs ovan fungerar emellertid inte bra när det är dåliga GNSS-förutsättningar, till exempel i tunnlar eller i täta skogar. Då behövs det annan data, och i detta arbete har användningen av kända punkter analyserats. De kända punkterna har implementerats under en sträcka med simulerad avsaknad av GNSS där ett tidsbaserat avstånd på 2, 5, 10, 20 och 40 sekunder mellan kända punkter har använts. Baserat på dessa experiment har en precision på 0,5 cm till 30 cm uppnåtts beroende på avståndet mellan de kända punkterna. Författarna rekommenderar att kända punkter ska implementeras i en tightly coupled beräkningsmetod var 5-10 sekund för att nå en noggrannhet på mm- cm nivå. Nackdelarna med kända punkter är däremot flertaliga eftersom de måste etableras och underhållas, vilket kräver både tid och pengar. Det är därför viktigt att minimera behovet av kända punkter. Det kan åstadkommas genom att till exempel implementera rälsdata i Kalmanfiltret, men även genom att öka effektiviteten i användandet av de kända punkterna. I det här arbetet rekommenderas det att undersöka hur det går att använda samma kända punkt flertalet gånger. Detta genom att antingen använda mer avancerade sensorer för att lokalisera de kända punkterna eller genom att öka antalet sensorer samt att sprida ut dem över fordonet. 3 Terms and Abbreviations DR = Dead Reckoning. A process of calculating the current position of a vehicle by using previous known positions. GCP = Ground Control Point. Point that has been established and measured so that its coordinates in a certain coordinate system is known. GNSS = Global Navigation Satellite Systems. An umbrella term used for satellite-based position and navigation systems such as GPS, GLONASS, Galileo and Beidou. INS = Inertial Navigation System. A system that uses a computer, accelerometers and gyroscopes to calculate acceleration, velocity and orientation of its host vehicle. IMU = Inertial Measurement Unit. A unit consisting of accelerometers and gyroscopes that measures the host vehicle's acceleration, velocity and orientation. LC = Loose Coupling/Loosely Coupled. A decentralized integration method where the individual systems are individually computed and then combined. MLSS = Mobile Laser Scanning System. A laser scanning system mounted on a moving vehicle. MMS = Mobile Mapping System. A combination of navigation tools and remote sensing tools used to collect and georeference data. TC = Tight Coupling/Tightly Coupled. A centralized integration method where the individual systems raw data are computed with each other in a filter. WS = Week Seconds. A week measured in seconds, goes from 0-604800 seconds. WSP= Williams Sale Partnership Limited. A global consulting company within engineering and infrastructure. ZUPT= Zero Velocity Update. A method consisting of having zero velocity to eliminate errors in INS drift. 4 Table of Contents Acknowledgements ........................................................................................................................................ 1 Abstract ........................................................................................................................................................... 2 Sammanfattning ............................................................................................................................................