Дисертації з теми "Inertial navigation; Kalman filter"
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Rogers, Jonas Paul. "GNSS and Inertial Fused Navigation Filter Simulation." Digital WPI, 2018. https://digitalcommons.wpi.edu/etd-theses/1303.
Повний текст джерелаMarquis, Carl W. "Integration of differential GPS and inertial navigation using a complementary Kalman filter /." Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1993. http://handle.dtic.mil/100.2/ADA273370.
Повний текст джерелаThesis advisor(S): Kaminer, Isaac I. "September 1993." Includes bibliographical references. Also available online.
Marquis, Carl W. III. "Integration of differential GPS and inertial navigation using a complementary Kalman filter." Thesis, Monterey, California. Naval Postgraduate School, 1993. http://hdl.handle.net/10945/39974.
Повний текст джерелаPrecise navigation with high update rates is essential for automatic landing of an unmanned aircraft. Individual sensors currently available - INS, AHRS, GPS, LORAN, etc. - cannot meet both requirements. The most accurate navigation sensor available today is the Global Positioning System or GPS. However, GPS updates only come once per second. INS, being an on-board sensor, is available as often as necessary. Unfortunately, it is subject to the Schuler cycle, biases, noise floor, and cross-axis sensitivity. In order to design and verify a precise, high update rate navigation system, a working model of Differential GPS has been developed including all of the major GPS error sources - clock differences, atmospherics, selective availability and receiver noise. A standard INS system was also modeled, complete with the inaccuracies mentioned. The outputs of these two sensors - inertial acceleration and pseudoranges - can be optimally blended with a complementary Kalman filter for positioning. Eventually, in the discrete case, the high update rate and high precision required for autoland can be achieved.
Abdul, Sattar H. L. "An adaptive U-D factorized Kalman filter for strap down inertial navigation system." Thesis, Cranfield University, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.237549.
Повний текст джерелаHartana, Pande. "Comparison of linearized and extended Kalman filter in GPS-aided inertial navigation system." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0015/MQ57729.pdf.
Повний текст джерелаHartana, Pande (Pande Putu Gde) Carleton University Dissertation Engineering Mechanical and Aerospace. "Comparison of linearized and extended Kalman filter in GPS aided inertial navigation system." Ottawa, 2000.
Знайти повний текст джерелаAkcay, Emre Mustafa. "Land Vehicle Navigation With Gps/ins Sensor Fusion Using Kalman Filter." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/2/12610327/index.pdf.
Повний текст джерелаs surface independent of his position. Yet, there are some conditions that the receiver encounters difficulties, such as weather conditions and some blockage problems due to buildings, trees etc. Due to these difficulties, GPS receivers&rsquo
errors increase. On the other hand, IMU works with respect to Newton&rsquo
s laws. Thus, in stark contrast with other navigation sensors (i.e. radar, ultrasonic sensors etc.), it is not corrupted by external signals. Owing to this feature, IMU is used in almost all navigation applications. However, it has some disadvantages such as possible alignment errors, computational errors and instrumentation errors (e.g., bias, scale factor, random noise, nonlinearity etc.). Therefore, a fusion or integration of GPS and IMU provides a more accurate navigation data compared to only GPS or only IMU navigation data. v In this thesis, loosely coupled GPS/IMU integration systems are implemented using feed forward and feedback configurations. The mechanization equations, which convert the IMU navigation data (i.e. acceleration and angular velocity components) with respect to an inertial reference frame to position, velocity and orientation data with respect to any desired frame, are derived for the geographical frame. In other words, the mechanization equations convert the IMU data to the Inertial Navigation System (INS) data. Concerning this conversion, error model of INS is developed using the perturbation of the mechanization equations and adding the IMU&rsquo
s sensor&rsquo
s error model to the perturbed mechanization equation. Based on this error model, a Kalman filter is constructed. Finally, current navigation data is calculated using IMU data with the help of the mechanization equations. GPS receiver supplies external measurement data to Kalman filter. Kalman filter estimates the error of INS using the error mathematical model and current navigation data is updated using Kalman filter error estimates. Within the scope of this study, some real experimental tests are carried out using the software developed as a part of this study. The test results verify that feedback GPS/INS integration is more accurate and reliable than feed forward GPS/INS. In addition, some tests are carried out to observe the results when the GPS receiver&rsquo
s data lost. In these tests also, the feedback GPS/INS integration is observed to have better performance than the feed forward GPS/INS integration.
Magree, Daniel Paul. "Monocular vision-aided inertial navigation for unmanned aerial vehicles." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53892.
Повний текст джерелаGautam, Ishwor. "Quaternion based attitude estimation technique involving the extended Kalman filter." University of Akron / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=akron1556196539847396.
Повний текст джерелаEddy, Joshua Galen. "A Hardware-Minimal Unscented Kalman Filter Framework for Visual-Inertial Navigation of Small Unmanned Aircraft." Thesis, Virginia Tech, 2017. http://hdl.handle.net/10919/77927.
Повний текст джерелаMaster of Science
Štefanisko, Ivan. "Integration of inertial navigation with global navigation satellite system." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2015. http://www.nusl.cz/ntk/nusl-221167.
Повний текст джерелаKirimlioglu, Serdar. "Multisensor Dead Reckoning Navigation On A Tracked Vehicle Using Kalman Filter." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614939/index.pdf.
Повний текст джерелаsome other sensors aid the navigation calculations. The aiding or fusion of sensors is accomplished via Kalman filter. In this thesis, a navigation algorithm and a sensor fusion algorithm were written. The sensor fusion algorithm is based on estimation of IMU errors by use of a Kalman filter. The design of Kalman filter is possible after deriving the mathematical model of error propagation of mechanization equations. For the sensor fusion, an IMU, two incremental encoders and a digital compass were utilized. The digital compass outputs the orientation data directly (without integration). In order to find the position, encoder data is calculated in dead reckoning sense. The sensor triplet aids the IMU which calculates position data by integrations. In order to mount these four sensors, an unmanned tracked vehicle prototype was manufactured. For data acquisition, an xPC&ndash
Target system was set. After planning the test procedure, the tests were performed. In the tests, different paths for different sensor fusion algorithms were experimented. The results were recorded in a computer and a number of figures were plotted in order to analyze the results. The results illustrate the benefit of sensor fusion and how much feedback sensor fusion is better than feed forward sensor fusion.
Barrett, Justin Michael. "Analyzing and Modeling Low-Cost MEMS IMUs for use in an Inertial Navigation System." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-theses/581.
Повний текст джерелаJönsson, Kenny. "Position Estimation of Remotely Operated Underwater Vehicle." Thesis, Linköping University, Automatic Control, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-57518.
Повний текст джерелаThis thesis aims the problem of underwater vehicle positioning. The vehicle usedwas a Saab Seaeye Falcon which was equipped with a Doppler Velocity Log(DVL)manufactured by RD Instruments and an inertial measurement unit (IMU) fromXsense. During the work several different Extended Kalman Filter (EKF) havebeen tested both with a hydrodynamic model of the vehicle and a model withconstant acceleration and constant angular velocity. The filters were tested withdata from test runs in lake Vättern. The EKF with constant acceleration andconstant angular velocity appeared to be the better one. The misalignment of thesensors were also tried to be estimated but with poor result.
Hirning, James L. "Optimal Kalman Filter Integration of a Global Positioning System Receiver and an LN-94 Inertial Navigation System." Thesis, Monterey, California. Naval Postgraduate School, 1990. http://hdl.handle.net/10945/37563.
Повний текст джерелаMalik, Zohaib Mansoor. "Design and implementation of temporal filtering and other data fusion algorithms to enhance the accuracy of a real time radio location tracking system." Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-13261.
Повний текст джерелаKapaldo, Aaron J. "Gyroscope Calibration and Dead Reckoning for an Autonomous Underwater Vehicle." Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/34418.
Повний текст джерелаMaster of Science
Rågmark, Johan. "Calibration and Evaluation of Inertial Navigation with Zero Velocity Update for Industrial Fastening Tools." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-298025.
Повний текст джерелаKvalitetssäkring är en central fråga för många tillverkningsindustrier, så som flygplans- och bilindustrin, där det är avgörande att varje förband har dragits åt på rätt sätt för att garantera säkerheten i produkten. Moderna fabriker har centrala styrsystem som kommunicerar med maskiner och verktyg, och ifall något blir fel är det vanligt att fabrikslinan stannar vilket blir kostsamt. Inomhuspositionering (IPS) av hög noggrannhet kan spåra vilken åtdragning som blivit fel, vilket dokumenteras och åtgärdas om möjligt senare, utan att stanna fabrikslinan. Dagens noggranna IPS system för kvalitetssäkring kräver installation och kalibrering av kameror och/eller antenner. Tröghetsnavigering kräver i grunden bara billiga sensorer installerade på verktyget men metoden är mycket opålitlig på grund av sensorernas opålitlighet och brus. I detta projekt har en metod för tröghetsnavigering, användandes Zero Velocity Update (ZUPT), evaluerats för kvalitetssäkring av handhållna verktyg genom repetabilitetstester. Tröghetsnavigeringsalgoritmen som tidigare utvecklats på Atlas Copco lyckas på effektivt sätt reducera drift och uppnår rimlig noggranhet för enklare rörelser. För linjära rörelser med rotationer under 45◦ så erhålls ett absolut positionsfel inom 10%. För att fungera väl även för mer komplexa rörelser krävs frekventa stop, och noggrann rörelsedetektion är central. Denna ZUPT-metod kommer aldrig att kunna generera position i realtid och algoritmen presterar väl endast inom vissa begränsningar. Trots detta så finns god potential för metoden inom kvalitetssäkring för handhållna industriverktyg.
Bacilieri, Federico. "Solutions and algorithms for inertial navigation of railroad vehicles." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14209/.
Повний текст джерелаNilsson, Mattias, and Rikard Vinkvist. "Sensor Fusion Navigation for Sounding Rocket Applications." Thesis, Linköping University, Linköping University, Department of Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-15001.
Повний текст джерелаOne of Saab Space’s products is the S19 guidance system for sounding rockets.Today this system is based on an inertial navigation system that blindly calculatesthe position of the rocket by integrating sensor readings with unknown bias. Thepurpose of this thesis is to integrate a Global Positioning System (GPS) receiverinto the guidance system to increase precision and robustness. There are mainlytwo problems involved in this integration. One is to integrate the GPS with sensorfusion into the existing guidance system. The seconds is to get the GPS satellitetracking to work under extremely high dynamics. The first of the two problems issolved by using an Extended Kalman filter (EKF) with two different linearizations.One of them is uses Euler angles and the other is done with quaternions. Theintegration technique implemented in this thesis is a loose integration between theGPS receiver and the inertial navigation system. The main task of the EKF isto estimate the bias of the inertial navigation system sensors and correct it toeliminate drift in the position. The solution is verified by computing the positionof a car using a GPS and an inertial measurement unit. Different solutions to theGPS tracking problem are proposed in a pre-study.
En av Saab Space produkter är navigationssystemet S19 som styr sondraketer.Fram till idag har systemet varit baserat på ett tröghetsnavigeringssystem somblint räknar ut position genom att integrera tröghetsnavigerinssystemets sensorermed okända biaser. Syftet med detta exjobb är att integrera en GPS med tröghetsnavigeringsystemetför att öka robusthet och precision. Det kan i huvudsak delasupp i två problem; att integrera en GPS-mottagare med det befintliga navigationsystemetmed användning utav sensorfusion, och att få satellitföljningen attfungera under extremt höga dynamiska förhållanden. Det första av de två problemenlöses genom ett Extended Kalman filter (EKF) med två olika linjäriseringar.Den första linjäriseringen är med Eulervinklar och är välbeprövad. Den andra ärmed kvaternioner. Integrationstekniken som implementeras i detta Examensarbeteär en lös integration mellan GPS-mottagaren och tröghetsnavigeringssystemet. Huvudsyftetmed EKF:en är att estimera bias i tröghetsnavigeringsystemets sensoreroch korrigera dem för att eliminera drifter i position. Lösningen verifieras genomatt räkna ut positionen för en bil med GPS och en inertiell mätenhet. Olika lösningartill satellitföljningen föreslås i en förstudie.
Nielsen, Jerel Bendt. "Robust Visual-Inertial Navigation and Control of Fixed-Wing and Multirotor Aircraft." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7584.
Повний текст джерелаKoroglu, Muhammed Taha. "Multiple Hypothesis Testing Approach to Pedestrian Inertial Navigation with Non-recursive Bayesian Map-matching." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1577135195323298.
Повний текст джерелаPerul, Johan. "Localisation autonome par apprentissage des dynamiques de déplacement en transport multimodal." Thesis, Ecole centrale de Nantes, 2020. http://www.theses.fr/2020ECDN0021.
Повний текст джерелаThe growing development of smart objects offers new opportunities for locating the connected traveller. However, tracking the trajectory of the pedestrian remains problematic and navigation applications do not offer to track the traveller's trajectory on a multimodal level in an autonomous way. This work focuses on the implementation of a single solution able to locate the user according to different travel modes and whatever the environment, using inertial, magnetic and GNSS sensors. In a first step, a new method for locating the cyclist is implemented. GNSS phase measurements are used to correct the velocity vector by time differences and the motion direction is constrained using inertial signals. These elements were used in a second step and adapted to implement a new method of pedestrian localization with a handheld sensor. The PDR approach, which is an inertial dead reckoning navigation technique, is parameterized in an extended Kalman filter. An innovative update merging the device attitude estimation and a statistical estimation of the walking direction allows to correct the walking heading estimation and obtain a consistent and smoothed estimation. GNSS measurements are used to correct speed vector, orientation, step length and absolute position. Finally, a multimodal approach is proposed and the management of transitions between the different algorithms, assisted by the use of an innovative sensor, is studied. Multimodal experimental validations in real conditions are conducted to analyze the estimation performances of the proposed solution
Kayasal, Ugur. "Modeling And Simulation Of A Navigation System With An Imu And A Magnetometer." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12608786/index.pdf.
Повний текст джерелаmeasurement equations of magnetometer for Kalman filtering are developed
the unique method to self align the MEMS navigation system is developed. In the motion estimation, the performance of the developed algorithms are compared using a GPS aided system and magnetometer aided system. Some experiments are conducted for self alignment algorithms.
Dill, Evan T. "GPS/Optical/Inertial Integration for 3D Navigation and Mapping Using Multi-copter Platforms." Ohio University / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1427382541.
Повний текст джерелаHuff, Joel E. "Absolute and Relative Navigation of an sUAS Swarm Using Integrated GNSS, Inertial and Range Radios." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1535040500005309.
Повний текст джерелаMcCrink, Matthew H. "Development of Flight-Test Performance Estimation Techniques for Small Unmanned Aerial Systems." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1449142886.
Повний текст джерелаSantana, Douglas Daniel Sampaio. "Navegação terrestre usando unidade de medição inercial de baixo desempenho e fusão sensorial com filtro de Kalman adaptativo suavizado." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/3/3152/tde-25082011-162939/.
Повний текст джерелаThis work presents the development of the mathematical models and the algorithms of a sensor fusion system for terrestrial navigation using a low-grade inertial measurement unit (IMU) and the Extended Kalman Filter. The models were developed on the basis of the strapdown inertial navigation systems (SINS). Low-grade designates an IMU that is not able to perform girocompassing self-alignment. The impossibility of navigating relying on a low performance IMU is the motivation for investigating techniques to improve the SINS accuracy with the use of additional sensors. This thesis describes the development of a comprehensive model of a sensor fusion for the inertial navigation of a ground vehicle using a low-grade IMU, an odometer and an electronic compass. Landmarks were placed along the test trajectory in order to allow the measurement of the error of the position estimation at these points. It is presented the development of the Smoothed Adaptive Kalman Filter (SAKF), which jointly estimates the states and the errors of the estimated states of the sensor fusion system. It is presented a quantitative criteria which employs the position uncertainties estimated by SAKF in order to determine - given the available sensors, the maximum time interval that one can navigate within a desired reliability. Reduced sets of landmarks are used as fictitious sensors to test the proposed reliability criterion. Also noteworthy are the mathematical models applied to terrestrial navigation that were unified in this work. The results show that, only relying on the low performance inertial sensors, the terrestrial navigation becomes impracticable after few tens of seconds. Using the same inertial sensors, the sensor fusion produced far better results, allowing the reconstruction of trajectories with displacements of about 2.7 km (or 15 minutes) with a final error of position estimation of about 3 m.
Zanoni, Fábio Doro. "Modelagem e implementação do sistema de navegação para um AUV." Universidade de São Paulo, 2012. http://www.teses.usp.br/teses/disponiveis/3/3152/tde-23032012-114741/.
Повний текст джерелаThis paper presents the study and implementation of a real-time navigation system used to estimate the position, velocity and attitude of an autonomous underwater vehicle. The Extended Kalman Filter, EKF, was adopted. This filter is often used to perform the data fusion from different sensors, in generating a statistically optimal estimate when some required conditions are fulfilled. The algorithm implements the fusion of the following sensors: an inertial navigation unit sensor (strapdown type), an acoustic positioning, a depth gauge, a Doppler velocity log sensor and a magnetic compass. This work presents the kinematic modelling, the methodology and computational tools used for developing the EKF algorithm. In order to integrate the EKF into an embedded system, it is necessary to develop it in real time. It was adopted the C / OS-II operational system, which allows to implement multithreaded systems and use traffic lights to manage the EKF. Furthermore, programming standards, such as MISRA C, was chosen to standardize the code and increase its reliability. The C code implementation took advantage of some Matlab functions to minimize implementation errors. Based on simulations and field tests carried out online, it was concluded that the filters designed to estimate the attitude and position of the vehicle provided good performances, in addition, it was possible to verify the EKFs convergence. The filters were tested in same adverse situations, e.g., a fault in the position reference sensor, providing satisfactory results as well.
Pálenská, Markéta. "Návrh algoritmu pro fúzi dat navigačních systémů GPS a INS." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2013. http://www.nusl.cz/ntk/nusl-230495.
Повний текст джерелаWu, Allen David. "Vision-based navigation and mapping for flight in GPS-denied environments." Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/37281.
Повний текст джерелаKorka, David A. (David Andrew) 1976. "Kalman filtering for aided inertial navigation system." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/9380.
Повний текст джерелаIncludes bibliographical references (p. 65).
This thesis develops a Kalman filter which integrates the inertial navigation system of the Vorticity Control Unmanned Undersea Vehicle (VCUUV) with redundant navigation sensor measurements. The model for the Kalman filter uses redundant measurements in a feedback loop to better estimate navigation variables. Using outputs from the Inertial Measurement Unit (IMU) and from a depth sensor, a velocity sensor and a magnetometer, a Kalman filter is developed. Actual test runs on the VCUUV prove the new system superior to the previously used open-loop navigation system.
by David A. Korka.
S.B.and M.Eng.
Yi, Yudan. "On improving the accuracy and reliability of GPS/INS-based direct sensor georeferencing." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1186671990.
Повний текст джерелаKossuth, Jonathan Andrew 1970. "The adaptive Kalman filter and micromechanical inertial instrument performance." Thesis, Massachusetts Institute of Technology, 1993. http://hdl.handle.net/1721.1/47335.
Повний текст джерелаJones, Philip Andrew. "Techniques in Kalman Filtering for Autonomous Vehicle Navigation." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/78128.
Повний текст джерелаMaster of Science
Aitken, Matthew Lawrence. "Lidar-Aided Inertial Navigation with Extended Kalman Filtering for Pinpoint Landing." NCSU, 2009. http://www.lib.ncsu.edu/theses/available/etd-07222009-105511/.
Повний текст джерелаFreeman, William John. "Digital Video Stabilization with Inertial Fusion." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/23080.
Повний текст джерелаThis thesis presents a digital stabilization method that incorporates inertial fusion with a Kalman filter. The camera motion is derived visually by tracking SIFT features in the video feed and fitting them to an affine model. The digital motion is fused with a 3 axis rotational motion measured by an inertial measurement unit (IMU) rigidly attached to the camera. The video is stabilized by digitally manipulating the image plane opposite of the unwanted motion.
The result is the foundation of a robust video stabilizer comprised of both visual and inertial measurements. The stabilizer is immune to dynamic scenes and requires less computation than current digital video stabilization methods.
Master of Science
Törnberg, Pontus. "GNSS independent navigation using radio navigation equipment." Thesis, Luleå tekniska universitet, Rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-81212.
Повний текст джерелаGalfond, Marissa N. (Marissa Nicole). "Visual-inertial odometry with depth sensing using a multi-state constraint Kalman filter." Thesis, Massachusetts Institute of Technology, 2014. http://hdl.handle.net/1721.1/97361.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 93-97).
The goal of visual inertial odometry (VIO) is to estimate a moving vehicle's trajectory using inertial measurements and observations, obtained by a camera, of naturally occurring point features. One existing VIO estimation algorithm for use with a monocular system, is the multi-state constraint Kalman filter (MSCKF), proposed by Mourikis and Li [34, 29]. The way the MSCKF uses feature measurements drastically improves its performance, in terms of consistency, observability, computational complexity and accuracy, compared to other VIO algorithms [29]. For this reason, the MSCKF is chosen as the basis for the estimation algorithm presented in this thesis. A VIO estimation algorithm for a system consisting of an IMU, a monocular camera and a depth sensor is presented in this thesis. The addition of the depth sensor to the monocular camera system produces three-dimensional feature locations rather than two-dimensional locations. Therefore, the MSCKF algorithm is extended to use the extra information. This is accomplished using a model proposed by Dryanovski et al. that estimates the 3D location and uncertainty of each feature observation by approximating it as a multivariate Gaussian distribution [11]. The extended MSCKF algorithm is presented and its performance is compared to the original MSCKF algorithm using real-world data obtained by flying a custom-built quadrotor in an indoor office environment.
by Marissa N. Galfond.
S.M.
Reece, Steven. "Qualitative model-based multi-sensor data fusion : the qualitative Kalman filter." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299237.
Повний текст джерелаLange, Sven. "Implementierung eines Mono-Kamera-SLAM Verfahrens zur visuell gestützten Navigation und Steuerung eines autonomen Luftschiffes." Master's thesis, Universitätsbibliothek Chemnitz, 2008. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-200800159.
Повний текст джерелаMotwani, Amit. "Interval Kalman filtering techniques for unmanned surface vehicle navigation." Thesis, University of Plymouth, 2015. http://hdl.handle.net/10026.1/3368.
Повний текст джерелаMatheson, Iggy. "Angles-Only EKF Navigation for Hyperbolic Flybys." DigitalCommons@USU, 2019. https://digitalcommons.usu.edu/etd/7608.
Повний текст джерелаHektor, Tomas. "Marginalized Particle Filter for Aircraft Navigation in 3-D." Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-10193.
Повний текст джерелаIn this thesis Sequential Monte Carlo filters, or particle filters, applied to aircraft navigation is considered. This report consists of two parts. The first part is an illustration of the theory behind this thesis project. The second and most important part evaluates the algorithm by using real flight data.
Navigation is about determining one's own position, orientation and velocity. The sensor fusion studied combines data from an inertial navigation system (INS) with measurements of the ground elevation below in order to form a terrain aided positioning system (TAP). The ground elevation measurements are compared with a height database. The height database is highly non-linear, which is why a marginalized particle filter (MPF) is used for the sensor fusion.
Tests have shown that the MPF delivers a stable and good estimate of the position, as long as it receives good data. A comparison with Saab's NINS algorithm showed that the two algorithms perform quite similar, although NINS performs better when data is lacking.
Aslan, Aydemir Gokcen. "Kalman Filter Based Fusion Of Camera And Inertial Sensor Measurements For Body State Estimation." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12611122/index.pdf.
Повний текст джерелаRosander, Peter. "Camera Based Terrain Navigation." Thesis, Linköping University, Linköping University, Automatic Control, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-16953.
Повний текст джерелаThe standard way for both ground and aerial vehicles to navigate is to use anInertial Navigation System, INS, containing an Inertial Measurement Unit, IMU,measuring the acceleration and angular rate, and a GPS measuring the position.The IMU provides high dynamic measurements of the acceleration and the angularrate, which the INS integrates to velocity, position and attitude, respectively.While being completely impossible to jam, the dead-reckoned estimates will driftaway, i.e., the errors are unbounded. In conjunction with a GPS, providing lowdynamic updates with bounded errors, a highly dynamic system without any driftis attained. The weakness of this system is its integrity, since the GPS is easilyjammed with simple equipment and powered only by a small standard battery.When the GPS is jammed this system falls back into the behavior of the INS withunbounded errors. To counter this integrity problem a camera can be used aseither a back up to the GPS or as its replacement. The camera provides imageswhich are then matched versus a reference, e.g., a map or an aerial photo, to getsimilar estimates as the GPS would provide. The camera can of course also bejammed by blocking the view of the camera with smoke. Bad visibility can alsooccur due to bad weather, but a camera based navigation system will definitelybe more robust than one using GPS.This thesis presents two ways to fuse the measurements from the camera and theIMU, both of them utilizing the Harris corner detector to find point correspondencesbetween the camera image and an aerial photo. The systems are evaluatedby simulated data mimicking both a low and a high accuracy IMU and a camerataking snapshots of the aerial photo. Results show that for the simulated cameraimages the implemented corner detector works fine and that the overall result iscomparable to using a GPS.
Standardsättet för både flygande och markgående fordon att navigera är att användaett tröghetsnavigeringssystem, innehållande en IMU som mäter acceleration ochvinkelhastighet, tillsammans med GPS. IMU:n tillhandahåller högfrekventa mätningarav acceleration och vinkelhastighet som integreras till hastighet, positionoch attityd. Ett sådant system är omöjligt att störa, men lider av att de dödräknadestorheterna hastighet, position och attityd, med tiden, kommer att driva ivägifrån de sanna värdena. Tillsammans med GPS, som ger lågfrekventa mätningarav positionen, erhålls ett system med god dynamik och utan drift. Svagheten i ettvvisådant system är dess integritet, då GPS enkelt kan störas med enkel och billigutrustning. För att lösa integritetsproblemet kan en kamera användas, antingensom stöd eller som ersättare till GPS. Kameran tar bilder som matchas gentemoten referens ex. en karta eller ett ortofoto. Det ger liknande mätningar som de GPSger. Ett kamerabaserat system kan visserligen också störas genom att blockerasynfältet för kameran med exempelvis rök. Dålig sikt kan också uppkomma pågrund av dåligt väder eller dimma, men ett kamerabaserat system kommer definitivtatt vara robustare än ett som använder GPS.Det här examensarbetet presenterar två sätt att fusionera mätningar från etttröghetssystem och en kamera. Gemensamt för båda är att en hörndetektor, Harriscorner detector, används för att hitta korresponderande punkter mellan kamerabildernaoch ett ortofoto. Systemen utvärderas på simulerat data. Resultatenvisar att för simulerade data så fungerar den implementerade hörndetektorn ochatt prestanda i nivå med ett GPS-baserat system uppnås.
Odom, Craig Allen. "Navigation solution for the Texas A&M autonomous ground vehicle." Texas A&M University, 2006. http://hdl.handle.net/1969.1/4244.
Повний текст джерелаLange, Sven Sünderhauf Niko. "Implementierung eines Mono-Kamera-SLAM Verfahrens zur visuell gestützten Navigation und Steuerung eines autonomen Luftschiffes." [S.l. : s.n.], 2008.
Знайти повний текст джерелаRamaswamy, Sridhar. "An investigation of integrarted Global Positioning System and inertial navigation system fault detection." Ohio : Ohio University, 2000. http://www.ohiolink.edu/etd/view.cgi?ohiou1172777336.
Повний текст джерелаLino, Rúben José Simões. "Low cost inertial-based localization system for a service robot." Master's thesis, Faculdade de Ciências e Tecnologia, 2011. http://hdl.handle.net/10362/5680.
Повний текст джерелаThe knowledge of a robot’s location it’s fundamental for most part of service robots. The success of tasks such as mapping and planning depend on a good robot’s position knowledge. The main goal of this dissertation is to present a solution that provides a estimation of the robot’s location. This is, a tracking system that can run either inside buildings or outside them, not taking into account just structured environments. Therefore, the localization system takes into account only measurements relative. In the presented solution is used an AHRS device and digital encoders placed on wheels to make a estimation of robot’s position. It also relies on the use of Kalman Filter to integrate sensorial information and deal with estimate errors. The developed system was testes in real environments through its integration on real robot. The results revealed that is not possible to attain a good position estimation using only low-cost inertial sensors. Thus, is required the integration of more sensorial information, through absolute or relative measurements technologies, to provide a more accurate position estimation.