Dissertations / Theses: '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.

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A navigation filter simulation and analysis environment was developed through the integration of DRAGON, a high fidelity real-time PNT sensor measurement source, and Scorpion, a modular navigation filter implementation framework. The envi- ronment allows navigation filters to be prototyped and tested in varying complex scenarios with a configurable set of navigation sensors including GNSS and IMU. An analysis of an EKF using the environment showed the utility and functionality of the system.

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Marquis, 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.

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Thesis (M.S. in Aeronautical Engineering) Naval Postgraduate School, September 1993.
Thesis advisor(S): Kaminer, Isaac I. "September 1993." Includes bibliographical references. Also available online.

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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.

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Approved for public release; distribution is unlimited.
Precise 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.

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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.

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Hartana, 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.

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Hartana, 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.

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7

Akcay, 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.

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Inertial Measurement Unit (IMU) and Global Positioning System (GPS) receivers are sensors that are widely used for land vehicle navigation. GPS receivers provide position and/or velocity data to any user on the Earth&rsquo
s 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.

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Magree, Daniel Paul. "Monocular vision-aided inertial navigation for unmanned aerial vehicles." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/53892.

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The reliance of unmanned aerial vehicles (UAVs) on GPS and other external navigation aids has become a limiting factor for many missions. UAVs are now physically able to fly in many enclosed or obstructed environments, due to the shrinking size and weight of electronics and other systems. These environments, such as urban canyons or enclosed areas, often degrade or deny external signals. Furthermore, many of the most valuable potential missions for UAVs are in hostile or disaster areas, where navigation infrastructure could be damaged, denied, or actively used against the vehicle. It is clear that developing alternative, independent, navigation techniques will increase the operating envelope of UAVs and make them more useful. This thesis presents work in the development of reliable monocular vision-aided inertial navigation for UAVs. The work focuses on developing a stable and accurate navigation solution in a variety of realistic conditions. First, a vision-aided inertial navigation algorithm is developed which assumes uncorrelated feature and vehicle states. Flight test results on a 80 kg UAV are presented, which demonstrate that it is possible to bound the horizontal drift with vision aiding. Additionally, a novel implementation method is developed for integration with a variety of navigation systems. Finally, a vision-aided navigation algorithm is derived within a Bierman-Thornton factored extended Kalman Filter (BTEKF) framework, using fully correlated vehicle and feature states. This algorithm shows improved consistency and accuracy by 2 to 3 orders of magnitude over the previous implementation, both in simulation and flight testing. Flight test results of the BTEKF on large (80 kg) and small (600 g) vehicles show accurate navigation over numerous tests.

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Gautam, 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.

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Eddy, 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.

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This thesis presents the development and implementation of a software framework for estimating the position of a drone during flight. This framework is based on an algorithm known as the Unscented Kalman Filter (UKF), a recursive method of estimating the state of a highly nonlinear system, such as an aircraft. In this thesis, we present a UKF formulation specially designed for a quadcopter carrying an Inertial Measurement Unit (IMU) and a downward-facing camera. The UKF fuses data from each of these sensors to track the position of the quadcopter over time. This work supports a number of similar efforts in the robotics and aerospace communities to navigate in GPS-denied environments with minimal hardware and minimal computational complexity. The software framework explored in this thesis provides a means for roboticists to easily implement similar UKF-based state estimators for a wide variety of systems, including surface vessels, undersea vehicles, and automobiles. We test the system's effectiveness by comparing its position estimates to those of a commercial motion capture system and then discuss possible applications.
Master of Science

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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.

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This paper deals with study of inertial navigation, global navigation satellite system, and their fusion into the one navigation solution. The first part of the work is to calculate the trajectory from accelerometers and gyroscopes measurements. Navigation equations calculate rotation with quaternions and remove gravity sensed by accelerometers. The equation’s output is in earth centred fixed navigation frame. Then, inertial navigation errors are discussed and focused to the bias correction. Theory about INS/GNSS inte- gration compares different integration architecture. The Kalman filter is used to obtain navigation solution for attitude, velocity and position with advantages of both systems.

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Kirimlioglu, 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.

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The aim of this thesis is to write a multisensor navigation algorithm and to design a test setup. After doing these, test the algorithm by using the test setup. In navigation, dead reckoning is a procedure to calculate the position from initial position with some measured inputs. These measurements do not include absolute position data. Using only an inertial measurement unit is an example for dead reckoning navigation. Calculating position and velocity with the inertial measurement unit is highly erroneous because, this calculation requires integration of acceleration data. Integration means accumulation of errors as time goes. For example, a constant acceleration error of 0.1 m/s^2 on 1 m/s^2 of acceleration will lead to 10% of position error in only 5 seconds. In addition to this, wrong calculation of attitude is going to blow the accumulated position errors. However, solving the navigation equations while knowing the initial position and the IMU readings is possible, the IMU is not used solely in practice. In literature, there are studies about this topic and in these studies
some 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.

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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.

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Inertial navigation is a relative navigation technique commonly used by autonomous vehicles to determine their linear velocity, position and orientation in three-dimensional space. The basic premise of inertial navigation is that measurements of acceleration and angular velocity from an inertial measurement unit (IMU) are integrated over time to produce estimates of linear velocity, position and orientation. However, this process is a particularly involved one. The raw inertial data must first be properly analyzed and modeled in order to ensure that any inertial navigation system (INS) that uses the inertial data will produce accurate results. This thesis describes the process of analyzing and modeling raw IMU data, as well as how to use the results of that analysis to design an INS. Two separate INS units are designed using two different micro-electro-mechanical system (MEMS) IMUs. To test the effectiveness of each INS, each IMU is rigidly mounted to an unmanned ground vehicle (UGV) and the vehicle is driven through a known test course. The linear velocity, position and orientation estimates produced by each INS are then compared to the true linear velocity, position and orientation of the UGV over time. Final results from these experiments include quantifications of how well each INS was able to estimate the true linear velocity, position and orientation of the UGV in several different navigation scenarios as well as a direct comparison of the performances of the two separate INS units.

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Jönsson, 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.

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This 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.

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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.

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This research develops and attempts to implement a Kalman filter integration of a Phase III Global Positioning System (GPS) five-channel receiver and an LN-94 Inertial Navigation System (INS). The GPS provides highly accurate position and velocity information in low dynamic environments. An INS provides position and velocity information with lower accuracy over long periods of time, but it is highly responsive in dynamic maneuvers or at high frequencies. The INS has the added advantage of requiring no signals external to the vehicle to function. The integration of these two systems provide more precise information under a wider variety of situations. A truth model for the INS is verified. A GPS error model is developed and combined with the INS model to provide GPS- aided INS navigation. This model is used to predict baseline performance of all full-ordered filter. Attempts are made to utilize the filter with empirical data. The data is analyzed, and suggestions are made about ways to account for the errors in evidence. Results to date are presented and analyzed. Keywords: Global positioning system, Inertial navigation system, GPS/INS Integration, GPS- aided-INS, Theses.

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Malik, 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.

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A general automotive navigation system is a satellite navigation system designed for use inautomobiles. It typically uses GPS to acquire position data to locate the user on a road in the unit's map database. However, due to recent improvements in the performance of small and lightweight micro-machined electromechanical systems (MEMS) inertial sensors have made the application of inertial techniques to such problems, possible. This has resulted in an increased interest in the topic of inertial navigation. In location tracking system, sensors are used either individually or in conjunction like in data fusion. However, still they remain noisy, and so there is a need to measure maximum data and then make an efficient system that can remove the noise from data and provide a better estimate. The task of this thesis work was to take data from two sensors, and use an estimation technique toprovide an accurate estimate of the true location. The proposed sensors were an accelerometer and a GPS device. This thesis however deals with using accelerometer sensor and using estimation scheme, Kalman filter. The thesis report presents an insight to both the proposed sensors and different estimation techniques. Within the scope of the work, the task was performed using simulation software Matlab. Kalman filter’s efficiency was examined using different noise levels.

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Kapaldo, Aaron J. "Gyroscope Calibration and Dead Reckoning for an Autonomous Underwater Vehicle." Thesis, Virginia Tech, 2005. http://hdl.handle.net/10919/34418.

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Autonomous Underwater Vehicles (AUVs) are currently being used for many underwater tasks such as mapping underwater terrain, detection of underwater objects, and assessment of water quality. Possible uses continue to grow as the vehicles become smaller, more agile, and less expensive to operate. However, trade-offs exist between making less expensive, miniature AUVs and the quality at which they perform. One area affected by cost and size is the onboard navigation system. To achieve the challenges of low-cost rate sensors, this thesis examines calibration methods that are suitable for identifying calibration coefficients in low-cost MEMS gyros. A brief introduction to underwater navigation is presented and is followed by the development of a model to describe the operation of a rate gyro. The model uses the integral relationship between angular rate and angular position measurements. A compass and two tilt sensors provide calibrated angular position data against which the three single axis gyros are compared to obtain an error signal describing errors present in the angular rate measurements. A calibration routine that adaptively identifies error parameters in the gyros is developed. Update laws are chosen to recursively apply estimated error parameters to minimize the system error signal. Finally, this calibration method is applied to a simple dead reckoning algorithm in an attempt to measure the improvements calibration provides.
Master of Science

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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.

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Indoor Positional Navigation (IPN) systems can be used to track the position of tools in factories which is crucial for quality assurance in many manufacturing industries. Inertial navigation is rarely used on its own because of the noisy Inertial Measurement Unit (IMU) sensors which contribute to large drift. Current IPN systems usually involve the installation and calibration of cameras or antennas, so achieving sufficient accuracy with inertial navigation based IPN would be very desirable. This project aims to evaluate an inertial navigation algorithm, based on Zero Velocity Update (ZUPT), for bolt level positioning by repeatability tests using an industrial robot. The ZUPT algorithm, developed at Atlas Copco, manages to effectively reduce drift and achieve moderate accuracy in position for simpler movements. The gravity tracking Kalman filter dictates the systematic errors in position that grow large with increased degree and dimension of rotation. When keeping rotations within 45◦ for a linear movement the absolute error in position is under 10%. Frequent stops are important when moving in a more complex trajectory to be able to negate drift, consequently detecting the start and stop of motion is crucial. The results show that increased frequency will improve accuracy. It is shown that averaging IMU samples before calculations can increase both truthfulness and precision by 10−25%, if sampling the IMU faster than the calculations. The ZUPT approach of inertial navigation will never yield positional results in real time, and the evaluated algorithm only performs well within certain limitations, mainly frequent stops and simple movements. Despite these limitations there is potential in using the algorithm for quality assurance purposes in hand held industrial fasteners.
Kvalitetssäkring 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.

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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/.

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Obiettivo di questa tesi è lo studio e lo sviluppo di soluzioni innovative di navigazione inerziale per applicazioni ferroviarie, strumento utile per il tracciamento del moto durante l'assenza prolungata di sistemi di localizzazione esterni, tipo GPS, come può avvenire in galleria. Definiti gli strumenti di lavoro, è stata poi eseguita un'analisi dello stato dell'arte al fine di mettere in evidenza le metodologie teoriche utilizzate, nonchè le prestazioni dei sistemi già esistenti. Sono poi caratterizzati i sensori e le misure disponibili. Sono proposte varie soluzioni al problema della navigazione inerziale, con l'obiettivo di valutarne le prestazioni durante periodi prolungati assenza del GPS e con varie condizioni al contorno. Dopo una prima versione basata su un singolo EKF, si è scelto di svilupparne una seconda classe in cui il problema di stimadi assetto (AHRS) e diposizione/velocità sono separati e risolti mediante due algoritmi distinti. È stato implementato un AHRS basato su EKF e uno mediante un osservatore non lineare; inoltre, sono stati sviluppati un EKF di ordine completo e uno ridotto per le dinamiche di traslazione. È stata poi sviluppata una soluzione per l'integrazione dei dati delle mappe, in modo da fornire correzioni più frequenti all'INS, mantenendo inoltre un ridotto carico computazionale e facilità di integrazione. Si è infine proceduto implementando e simulando la soluzione a singolo stadio e le varie combinazioni di INS a due stadi in ambiente Matlab-Simulink. Gli algoritmi a due stadi hanno mostrato in simulazione prestazioni migliori rispetto alla struttura a EKF singolo la quale presenta un dominio di convergenza troppo limitato per fini pratici. A conclusione del lavoro, svolto avvalendosi della collaborazione di Sadel, sono state gettate le basi per una successiva analisi atta a verificare se la struttura a due stadi consente la convergenza anche dei bias di accelerometro

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Nilsson, 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.

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One 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.

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Nielsen, Jerel Bendt. "Robust Visual-Inertial Navigation and Control of Fixed-Wing and Multirotor Aircraft." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7584.

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With the increased performance and reduced cost of cameras, the robotics community has taken great interest in estimation and control algorithms that fuse camera data with other sensor data.In response to this interest, this dissertation investigates the algorithms needed for robust guidance, navigation, and control of fixed-wing and multirotor aircraft applied to target estimation and circumnavigation.This work begins with the development of a method to estimate target position relative to static landmarks, deriving and using a state-of-the-art EKF that estimates static landmarks in its state.Following this estimator, improvements are made to a nonlinear observer solving part of the SLAM problem.These improvements include a moving origin process to keep the coordinate origin within the camera field of view and a sliding window iteration algorithm to drastically improve convergence speed of the observer.Next, observers to directly estimate relative target position are created with a circumnavigation guidance law for a multirotor aircraft.Taking a look at fixed-wing aircraft, a state-dependent LQR controller with inputs based on vector fields is developed, in addition to an EKF derived from error state and Lie group theory to estimate aircraft state and inertial wind velocity.The robustness of this controller/estimator combination is demonstrated through Monte Carlo simulations.Next, the accuracy, robustness, and consistency of a state-of-the-art EKF are improved for multirotors by augmenting the filter with a drag coefficient, partial updates, and keyframe resets.Monte Carlo simulations demonstrate the improved accuracy and consistency of the augmented filter.Lastly, a visual-inertial EKF using image coordinates is derived, as well as an offline calibration tool to estimate the transforms needed for accurate, visual-inertial estimation algorithms.The imaged-based EKF and calibrator are also shown to be robust under various conditions through numerical simulation.

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Koroglu, 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.

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Perul, Johan. "Localisation autonome par apprentissage des dynamiques de déplacement en transport multimodal." Thesis, Ecole centrale de Nantes, 2020. http://www.theses.fr/2020ECDN0021.

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Le développement croissant d'objets intelligents offre de nouvelles opportunités de localisation du voyageur connecté. Cependant, le suivi de la trajectoire du piéton reste problématique et les applications de navigation ne proposent pas de suivre la trajectoire du voyageur à l’échelle multimodale de façon autonome. Ce travail s’intéresse à la mise en place d’une solution unique capable de localiser l’utilisateur selon différents mode de déplacement et quel que soit l’environnement, à partir de capteurs inertiels, magnétique et GNSS. Dans un premier temps, une nouvelle méthode de localisation du cycliste est mise en place. Les mesures de phases GNSS sont utilisées pour corriger le vecteur vitesse par différences temporelles et la direction de déplacement est contrainte à l'aide des signaux inertiels. Ces éléments ont été utilisés dans un second temps et adaptés pour mettre en place une nouvelle méthode de localisation du piéton avec un capteur en main. L’approche PDR qui est une technique de navigation inertielle à l’estime est paramétrée dans un filtre de Kalman étendu. Une mise à jour innovante fusionnant l’estimation de l’attitude du boîtier et une estimation statistique de la direction de marche permet de corriger l’estimation du cap de marche et obtenir une estimation cohérente et lissée. Les mesures GNSS sont utilisées pour corriger le vecteur vitesse, l’orientation, la longueur de pas et la position absolue. Enfin, une approche multimodale est proposée et la gestion des transitions entre les différents algorithmes, assistée par l’utilisation d’un capteur innovant, est étudiée. Des validations expérimentales multimodales en conditions réelles sont conduites pour analyser les performances d’estimation de la solution proposée
The 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

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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.

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In this thesis, the integration of a MEMS based inertial measurement unit and a three axis solid state magnetometer are studied. It is a fact that unaided inertial navigation systems, especially low cost MEMS based navigation systems have a divergent behavior. Nowadays, many navigation systems use GPS aiding to improve the performance, but GPS may not be applicable in some cases. Also, GPS provides the position and velocity reference whereas the attitude information is extracted through estimation filters. An alternative reference source is a three axis magnetometer, which provides direct attitude measurements. In this study, error propagation equations of an inertial navigation system are derived
measurement 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.

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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.

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Huff, 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.

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McCrink, 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.

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Santana, 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/.

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Apresenta-se o desenvolvimento de modelos matemáticos e algoritmos de fusão sensorial para navegação terrestre usando uma unidade de medição inercial (UMI) de baixo desempenho e o Filtro Estendido de Kalman. Os modelos foram desenvolvidos com base nos sistemas de navegação inercial strapdown (SNIS). O termo baixo desempenho refere-se à UMIs que por si só não são capazes de efetuar o auto- alinhamento por girocompassing. A incapacidade de se navegar utilizando apenas uma UMI de baixo desempenho motiva a investigação de técnicas que permitam aumentar o grau de precisão do SNIS com a utilização de sensores adicionais. Esta tese descreve o desenvolvimento do modelo completo de uma fusão sensorial para a navegação inercial de um veículo terrestre usando uma UMI de baixo desempenho, um hodômetro e uma bússola eletrônica. Marcas topográficas (landmarks) foram instaladas ao longo da trajetória de teste para se medir o erro da estimativa de posição nesses pontos. Apresenta-se o desenvolvimento do Filtro de Kalman Adaptativo Suavizado (FKAS), que estima conjuntamente os estados e o erro dos estados estimados do sistema de fusão sensorial. Descreve-se um critério quantitativo que emprega as incertezas de posição estimadas pelo FKAS para se determinar a priori, dado os sensores disponíveis, o intervalo de tempo máximo que se pode navegar dentro de uma margem de confiabilidade desejada. Conjuntos reduzidos de landmarks são utilizados como sensores fictícios para testar o critério de confiabilidade proposto. Destacam-se ainda os modelos matemáticos aplicados à navegação terrestre, unificados neste trabalho. Os resultados obtidos mostram que, contando somente com os sensores inerciais de baixo desempenho, a navegação terrestre torna-se inviável após algumas dezenas de segundos. Usando os mesmos sensores inerciais, a fusão sensorial produziu resultados muito superiores, permitindo reconstruir trajetórias com deslocamentos da ordem de 2,7 km (ou 15 minutos) com erro final de estimativa de posição da ordem de 3 m.
This 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.

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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/.

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Este trabalho apresenta o estudo e a implementação de um sistema de navegação em tempo-real utilizado para estimar a posição, a velocidade e a atitude de um veículo submarino autônomo. O algoritmo investigado é o do Filtro de Kalman Estendido. Este filtro é freqüentemente usado para realizar a fusão de dados obtidos de diferentes sensores, em uma estimativa estatisticamente ótima, quando se respeita algumas condições. Neste trabalho, fez se a fusão entre os seguintes sensores: unidade de navegação inercial do tipo strapdown, sensor acústico de posicionamento, profundímetro, sensor de velocidade de efeito Doppler e uma bússola. Para a aplicação embarcada do Filtro de Kalman, faz-se necessário o seu desenvolvimento em tempo real. Conseqüentemente, este trabalho apresenta o estudo das principais características de um sistema de tempo real. Para desenvolver o código em C utilizou-se de algumas funções do Matlab com a finalidade de se tentar minimizar os erros de implementação do filtro. Além disto, para facilitar a implementação e respeitar os critérios de sistemas de tempo real utilizou-se de um sistema operacional, C/OS-II que possibilita aplicar sistemas com multiprocessos e utilizar semáforos para o gerenciamento do EKF, além disto, foram utilizadas normas de programação, MISRAC, para padronizar o código e aumentar a sua confiabilidade. São apresentadas também a modelagem cinemática, a metodologia e as ferramentas computacionais utilizadas para o filtro. Com base nas simulações e nos ensaios de campo executados on-line, observou-se que os filtros projetados para se estimar a atitude e a posição do veículo obtiveram bons desempenhos, além disto, foi possível verificar a convergência dos EKFs. Para estas simulações e ensaios, foram também estudados casos de situações adversas como, por exemplo, uma falha no sensor de referência de posição, sendo que para esta situação, o EKF de posição e velocidade obteve resultados satisfatórios.
This 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.

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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.

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Diplomová práce se zabývá návrhem algoritmu rozšířeného Kalmanova filtru, který integruje data z inerciálního navigačního systému (INS) a globálního polohovacího systému (GPS). Součástí algoritmu je i samotná mechanizace INS, určující na základě dat z akcelerometrů a gyroskopů údaje o rychlosti, zeměpisné pozici a polohových úhlech letadla. Vzhledem k rychlému nárůstu chybovosti INS je výstup korigován hodnotami rychlosti a pozice získané z GPS. Výsledný algoritmus je implementován v prostředí Simulink. Součástí práce je odvození jednotlivých stavových matic rozšířeného Kalmanova filtru.

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31

Wu, 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.

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Traditionally, the task of determining aircraft position and attitude for automatic control has been handled by the combination of an inertial measurement unit (IMU) with a Global Positioning System (GPS) receiver. In this configuration, accelerations and angular rates from the IMU can be integrated forward in time, and position updates from the GPS can be used to bound the errors that result from this integration. However, reliance on the reception of GPS signals places artificial constraints on aircraft such as small unmanned aerial vehicles (UAVs) that are otherwise physically capable of operation in indoor, cluttered, or adversarial environments. Therefore, this work investigates methods for incorporating a monocular vision sensor into a standard avionics suite. Vision sensors possess the potential to extract information about the surrounding environment and determine the locations of features or points of interest. Having mapped out landmarks in an unknown environment, subsequent observations by the vision sensor can in turn be used to resolve aircraft position and orientation while continuing to map out new features. An extended Kalman filter framework for performing the tasks of vision-based mapping and navigation is presented. Feature points are detected in each image using a Harris corner detector, and these feature measurements are corresponded from frame to frame using a statistical Z-test. When GPS is available, sequential observations of a single landmark point allow the point's location in inertial space to be estimated. When GPS is not available, landmarks that have been sufficiently triangulated can be used for estimating vehicle position and attitude. Simulation and real-time flight test results for vision-based mapping and navigation are presented to demonstrate feasibility in real-time applications. These methods are then integrated into a practical framework for flight in GPS-denied environments and verified through the autonomous flight of a UAV during a loss-of-GPS scenario. The methodology is also extended to the application of vehicles equipped with stereo vision systems. This framework enables aircraft capable of hovering in place to maintain a bounded pose estimate indefinitely without drift during a GPS outage.

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Korka, 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.

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Thesis (S.B. and M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1999.
Includes 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.

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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.

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Kossuth, 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.

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Jones, Philip Andrew. "Techniques in Kalman Filtering for Autonomous Vehicle Navigation." Thesis, Virginia Tech, 2015. http://hdl.handle.net/10919/78128.

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This thesis examines the design and implementation of the navigation solution for an autonomous ground vehicle suited with global position system (GPS) receivers, an inertial measurement unit (IMU), and wheel speed sensors (WSS) using the framework of Kalman filtering (KF). To demonstrate the flexibility of the KF several methods are explored and implemented such as constraints, multi-rate data, and cascading filters to augment the measurement matrix of a main filter. GPS and IMU navigation are discussed, along with common errors and disadvantages of each type of navigation system. It is shown that the coupling of sensors, constraints, and self-alignment techniques provide an accurate solution to the navigation problem for an autonomous vehicle. Filter divergence is discussed during times when the states are unobservable. Post processed data is analyzed to demonstrate performance under several test cases, such as GPS outage, and the effect that the initial calibration and alignment has on the accuracy of the solution.
Master of Science

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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/.

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In support of NASAâs Autonomous Landing and Hazard Avoidance Technology project, an extended Kalman filter (EKF) routine has been developed for estimating the position, velocity, and attitude of a spacecraft during the landing phase of a planetary mission. The EKF is a recursive algorithm for obtaining the minimum variance estimate of a nonlinear dynamic process from a sequence of noisy observations. The proposed filter combines measurements of acceleration and angular velocity from an inertial measurement unit with range and range-rate observations from an onboard light detection and ranging (LIDAR) system. These high-precision LIDAR measurements of distance to the ground and approach velocity will enable both robotic and manned vehicles to land safely and precisely at scientifically interesting sites. The robustness and accuracy of the Kalman filter were first established using a simplified simulation of the final translation and touchdown phase of the Apollo lunar landings. In addition, experimental results from a helicopter flight test performed at NASA Dryden in August 2008 demonstrate the merit in employing LIDAR for pinpoint landing in future space missions.

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Freeman, William John. "Digital Video Stabilization with Inertial Fusion." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/23080.

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As computing power becomes more and more available, robotic systems are moving away from active sensors for environmental awareness and transitioning into passive vision sensors. With the advent of teleoperation and real-time video tracking of dynamic environments, the need to stabilize video onboard mobile robots has become more prevalent.

This 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

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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.

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This thesis studies algorithms to estimate an aircraft’s position with different information from various radio stations. Because aircrafts both civilian and military are heavily dependant on GNSS signals, it can be interfered from hostile sources. The aircraft shall then be able to navigate without the GNSS signals. This thesis focuses on three radio navigation systems, DME,VOR and TACAN. With the measurements from these three radio stations and measurements from the inertial navigation system one can estimate a position with an estimation filter. In this thesis two types of filters will be used, the linear Kalman filter and the Extended Kalman filter. The linear Kalman filter will be used when converting the TACAN measurements to a pseudo position and the Extended Kalman filter will be used for the DME,VOR and TACAN measurements. The results shows that the converted TACAN measurements and TACAN measurements estimates very well in both north and east direction. When using only DME measurements the filter estimates the position fairly well in the direction towards the station and poorly in the orthogonal direction. For the VOR measurements the filter estimates the position quite poorly in the direction of the radio station and well in the orthogonal direction. In conclusion the converted TACAN measurement and TACAN measurement algorithm can be used for navigation purposes by its own measurements. However, the DME and VOR measurement algorithms need to be combined or using multiple stations at different locations to get better estimates in both directions. All of the filter could use some better tuning to get the optimal filter, but it is not necessary.

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Galfond, 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.

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Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2014.
Cataloged 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.

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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.

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Lange, 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.

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Kamerabasierte Verfahren zur Steuerung autonomer mobiler Roboter wurden in den letzten Jahren immer populärer. In dieser Arbeit wird der Einsatz eines Stereokamerasystems und eines Mono-Kamera-SLAM Verfahrens hinsichtlich der Unterstützung der Navigation eines autonomen Luftschiffes untersucht. Mit Hilfe von Sensordaten aus IMU, GPS und Kamera wird eine Positionsschätzung über eine Sensorfusion mit Hilfe des Extended und des Unscented Kalman Filters durchgeführt.

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Motwani, Amit. "Interval Kalman filtering techniques for unmanned surface vehicle navigation." Thesis, University of Plymouth, 2015. http://hdl.handle.net/10026.1/3368.

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This thesis is about a robust filtering method known as the interval Kalman filter (IKF), an extension of the Kalman filter (KF) to the domain of interval mathematics. The key limitation of the KF is that it requires precise knowledge of the system dynamics and associated stochastic processes. In many cases however, system models are at best, only approximately known. To overcome this limitation, the idea is to describe the uncertain model coefficients in terms of bounded intervals, and operate the filter within the framework of interval arithmetic. In trying to do so, practical difficulties arise, such as the large overestimation of the resulting set estimates owing to the over conservatism of interval arithmetic. This thesis proposes and demonstrates a novel and effective way to limit such overestimation for the IKF, making it feasible and practical to implement. The theory developed is of general application, but is applied in this work to the heading estimation of the Springer unmanned surface vehicle, which up to now relied solely on the estimates from a traditional KF. However, the IKF itself simply provides the range of possible vehicle headings. In practice, the autonomous steering system requires a single, point-valued estimate of the heading. In order to address this requirement, an innovative approach based on the use of machine learning methods to select an adequate point-valued estimate has been developed. In doing so, the so called weighted IKF (wIKF) estimate provides a single heading estimate that is robust to bounded model uncertainty. In addition, in order to exploit low-cost sensor redundancy, a multi-sensor data fusion algorithm compatible with the wIKF estimates and which additionally provides sensor fault tolerance has been developed. All these techniques have been implemented on the Springer platform and verified experimentally in a series of full-scale trials, presented in the last chapter of the thesis. The outcomes demonstrate that the methods are both feasible and practicable, and that they are far more effective in providing accurate estimates of the vehicle’s heading than the conventional KF when there is uncertainty in the system model and/or sensor failure occurs.

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Matheson, Iggy. "Angles-Only EKF Navigation for Hyperbolic Flybys." DigitalCommons@USU, 2019. https://digitalcommons.usu.edu/etd/7608.

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Space travelers in science fiction can drop out of hyperspace and make a pinpoint landing on any strange new world without stopping to get their bearings, but real-life space navigation is an art characterized by limited information and complex mathematics that yield no easy answers. This study investigates, for the first time ever, what position and velocity estimation errors can be expected by a starship arriving at a distant star - specifically, a miniature probe like those proposed by the Breakthrough Starshot initiative arriving at Proxima Centauri. Such a probe consists of nothing but a small optical camera and a small microprocessor, and must therefore rely on relatively simple methods to determine its position and velocity, such as observing the angles between its destination and certain guide stars and processing them in an algorithm known as an extended Kalman filter. However, this algorithm is designed for scenarios in which the position and velocity are already known to high accuracy. This study shows that the extended Kalman filter can reliably estimate the position and velocity of the Starshot probe at speeds characteristic of current space probes, but does not attempt to model the filter’s performance at speeds characteristic of Starshot-style proposals. The gravity of the target star is also estimated using the same methods.

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Hektor, 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.

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In 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.

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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.

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The focus of the present thesis is on the joint use of cameras and inertial sensors, a recent area of active research. Within our scope, the performance of body state estimation is investigated with isolated inertial sensors, isolated cameras and finally with a fusion of two types of sensors within a Kalman Filtering framework. The study consists of both simulation and real hardware experiments. The body state estimation problem is restricted to a single axis rotation where we estimate turn angle and turn rate. This experimental setup provides a simple but effective means of assessing the benefits of the fusion process. Additionally, a sensitivity analysis is carried out in our simulation experiments to explore the sensitivity of the estimation performance to varying levels of calibration errors. It is shown by experiments that state estimation is more robust to calibration errors when the sensors are used jointly. For the fusion of sensors, the Indirect Kalman Filter is considered as well as the Direct Form Kalman Filter. This comparative study allows us to assess the contribution of an accurate system dynamical model to the final state estimates. Our simulation and real hardware experiments effectively show that the fusion of the sensors eliminate the unbounded error growth characteristic of inertial sensors while final state estimation outperforms the use of cameras alone. Overall we can v demonstrate that the Kalman based fusion result in bounded error, high performance estimation of body state. The results are promising and suggest that these benefits can be extended to body state estimation for multiple degrees of freedom.

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46

Rosander, 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.

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The 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.

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47

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.

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The need addressed in this thesis is to provide an Autonomous Ground Vehicle (AGV) with accurate information regarding its position, velocity, and orientation. The system chosen to meet these needs incorporates (1) a differential Global Positioning System, (2) an Inertial Measurement Unit consisting of accelerometers and angular-rate sensors, and (3) a Kalman Filter (KF) to fuse the sensor data. The obstacle avoidance software requires position and orientation to build a global map of obstacles based on the returns of a scanning laser rangefinder. The path control software requires position and velocity. The development of the KF is the major contribution of this thesis. This technology can either be purchased or developed, and, for educational and financial reasons, it was decided to develop instead of purchasing the KF software. This thesis analyzes three different cases of navigation: one-dimensional, two dimensional and three-dimensional (general). Each becomes more complex, and separating them allows a three step progression to reach the general motion solution. Three tests were conducted at the Texas A&M University Riverside campus that demonstrated the accuracy of the solution. Starting from a designated origin, the AGV traveled along the runway and then returned to the same origin within 11 cm along the North axis, 19 cm along the East axis and 8 cm along the Down axis. Also, the vehicle traveled along runway 35R which runs North-South within 0.1ÃÂ°, with the yaw solution consistently within 1ÃÂ° of North or South. The final test was mapping a box onto the origin of the global map, which requires accurate linear and angular position estimates and a correct mapping transformation.

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48

Lange, Sven Sünderhauf Niko. "Implementierung eines Mono-Kamera-SLAM Verfahrens zur visuell gestützten Navigation und Steuerung eines autonomen Luftschiffes." [S.l. : s.n.], 2008.

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49

Ramaswamy, 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.

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Lino, 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.

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Dissertation presented at Faculty of Sciences and Technology of the New University of Lisbon to attain the Master degree in Electrical and Computer Science Engineering
The 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.

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Dissertations / Theses on the topic 'Inertial navigation; Kalman filter'

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