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Desgardin, Aurelie D. "EKLF-meidated transcription of erythroid genes." Thesis, Kingston University, 2010. http://eprints.kingston.ac.uk/20434/.
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Erythroid Kruppel-like factor (EKLF) is an erythroid specific transcription factor that binds to the proximal [beta]-globin gene promoter and is essential for high level expression. In addition, EKLF binds to the far upstream enhancer commonly referred to as the Locus Control Region (LCR). The nature of these two events, their relationship to other events at the multigene [beta]-globin locus and the precise and required interaction of these cis-acting sequences remains unclear. Equally, the mode of action ofEKLF at other erythroid-specific gene loci that are not regulated by an LCR has not yet been reported. These targets include chaperones, membrane-bound proteins, and enzymes of the heme biosynthesis pathway that are essential for red blood cell function. To elucidate the role of EKLF at the multigene locus, and at other erythroid genes, we monitored the temporal EKLF-directed events across the [beta]-globin locus, at the AHSP and Dematin as well as the ALAD promoter using a unique 4-0H-Tamoxifen EKLF-inducible erythroid cell line (JH31), developed in the laboratory, chromatin immunoprecipitation (ChIP) studies, and DNasel hypersensitivity assays. We demonstrate here that EKLF is not required for priming of the [beta]-globin locus for expression. However, EKLF is essential for maximal erythroid factor occupancy, recruitment of chromatin-modifying enzymes, and effective recruitment of the RNA Polymerase II complex. We show that EKLF recruits these complexes first to the LCR prior to the [beta]-globin promoter, suggesting that the LCR serves as a docking element. Finally we provide evidence that the LCR/promoter interacting factor, Ldb-l, is recruited to the [beta]-promoter in an EKLF-dependent manner. We extend our observations to several EKLF-regulated genes outside the [beta]-globin cluster, demonstrating not only the kinetics of transcriptional activation, but also a previously unknown mechanism of chromatin remodeling that implicates histone eviction. Finally, we report a discrepancy between the roles of the histone acetyltransferases CBP and p300 at EKLF target gene promoters, challenging conservative notions of basic transcriptional events. Together, our observations deepen our understanding of the mechanisms of action of EKLF, and provide a platform for additional studies.
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Lung, Tina Kathy. "Analysis of Mouse EKLF/KLF2 E9.5 Double Knockout: Yolk Sac Morphology and Embryonic Erythroid Maturation." VCU Scholars Compass, 2007. http://hdl.handle.net/10156/1821.
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Bouilloux, Fabrice. "Rôle du facteur de transcription EKLF dans le contrôle de la fourche de différenciation érrythro-mégacaryocytaire." Lyon 1, 2008. http://n2t.net/ark:/47881/m6833q4v.
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EKLF et FLI-1 sont deux facteurs de transcription antagonistes activant respectivement la transcription des gènes spécifiques des lignées érythrocytaire et mégacaryocytaire qui dérivent d'un progéniteur bipotent commun. L'absence de EKLF favorise l'immortalisation des cellules érythrocytaires et la production dérégulée de FLI-1 est une cause récurrente de leur transformation. Afin de préciser le rôle de EKLF, son expression a été modulée dans des cellules érythroleucémiques ou dans des progéniteurs multipotents hématopoïétiques humains. Nos résultats montrent que EKLF n'a d'effet antiprolifératif ni dans des cellules érythroleucémiques surexprimant FLI-1 ni dans des progéniteurs natifs, mais favorise l'engagement du progéniteur bipotent vers la différenciation érythrocytaire au détriment de celle mégacaryocytaire. Ce rôle de EKLF ne dépend pas de sa capacité à lier l'ADN, mais s'explique au moins en partie par l'inhibition du recrutement de FLI-1 sur les promoteurs mégacaryocytairesEKLF and FLI-1 are antagonistic transcription factors driving the specific gene expression of erythrocytic and megakaryocytic lineage respectively, which derive from a common bipotent progenitor. FLI-1 overexpression is observed recurrently in Friend erythroleukemia and immortalisation of the erythrocytic lignage is favoured by EKLF knock out. In order to study the EKLF functions, its expression level has been modulated in erythroleucemic cells and in human mulitpotent hematopoietic progenitors. Our results failed to evidence any antiproliferation effect of EKLF neither in Friend erythroleukemic cells nor in multipotent prognitors. On the other hand, EKLF favours the erythrocytic at the expense of megakaryocytic differentiation of the bipotent progenitor. This effect is not dependent on the EKLF DNA binding ability and can be explained, at least in part, by the inhibition of FLI-1 recruitment on megakaryocytic promoters
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Richardson, Ben Matthew. "A proteomic approach to characterising erythropoiesis in both normal and In(Lu) phenotypes, with investigations into EKLF function." Thesis, University of Bristol, 2008. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.492444.
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Erythropoiesis is the process in which a pluripotent stem cell differentiates into a mature red blood cell (RBC). Over the last two decades understanding of factors that regulate this process has increased, which has allowed for the development of advanced in vitro culture systems that can on a small scale, generate therapeutic quantities of mature RBCs from isolated stem cell populations. Not only does this create an alternative source of RBCs for use in medical treatments, it also provides a esearch model that can be used to investigate the underlying biochemical and physiological mechanisms of human erythropoiesis.
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Ma, Rui. "Optimizing the production of erythroid cells from human embryonic stem cells." Thesis, University of Edinburgh, 2015. http://hdl.handle.net/1842/17979.
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Red blood cell (RBC) transfusion is the major treatment for patients suffering from trauma or severe anaemias, and life-long transfusion may be needed to alleviate symptoms and maintain body functioning. However, with a relatively low portion of people are donating, shortage in blood supply is becoming a life-threatening issue in the aging society. Among attempts to identify novel sources for transfusion medicine, human pluripotent stem cells (hPSCs), including embryonic stem cells (hESCs) and induced pluripotent stem cells (hiPSCs) are currently the most promising candidate, which is capable of producing donor-independent, pathogen-free and immunologically compatible RBCs. Currently, hESC-derived erythropoiesis in vitro is considered to mimic the very primitive yolk sac haematopoiesis, indicated by a low or absent level of β globin production and incomplete enucleation. Thus these cells are not mature enough to be used in transfusion medicine. The aim of this PhD project was to overexpress a key erythroid transcription factor, Erythroid Krüppel-like factor (EKLF or KLF1) in an inducible manner to improve the maturation of hESC-derived erythroid cells. EKLF is a member of the Krüppel-like factor family, which is characterized by three C2H2 type zinc finger motifs. EKLF expression in vivo is highly restricted to erythroid cells in yolk sac, fetal liver, spleen and the bone marrow, although recently a low-level of expression was found in haematopoietic precursors. Published reports demonstrate that EKLF can 1) activate β globin expression by binding to the CACCC box in its promoter or by altering β-like globin locus chromatin structure; 2) exert a role in MEP (common progenitor for erythrocytes and megakaryocytes) stage by favouring erythroid differentiation against megakaryocyte differentiation; 3) promote enucleation by affecting the DNase II-alpha expression in the central macrophage of a fetal liver erythroblastic island; 4) act as an instructive factor for lineage commitment towards erythroid fate in HSCs. In this project, 1) We tested and evaluated a feeder-free, serum-free differentiation system for deriving erythroid cells from hESCs; 2) We constructed constitutive and inducible EKLF expression vectors and validated them in K562 cells; 3) We generated hESC lines carrying these EKLF expression vectors and assessed their effects on erythrocyte production and maturation. We found that our differentiation system was capable of generating haematopoietic progenitors (HPCs) and erythroid cells at high efficiency. Using this differentiation system, we concluded that enhanced expression of EKLF upregulated adult β globin expression selectively, without altering expressions of other globins. This finding provides hints for the development of novel approaches to “reprogramme” hESCs towards a certain fate and overexpression of EKLF in this differentiation system may be beneficial for resolving issues in future transfusion medicine.
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Lau, Winnie. "Studies of human EKLF mutations associated with the In(Lu) blood group phenotype and a rare variant of congenital dyserlthropoietic anaemia." Thesis, University of Bristol, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.529878.
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Jansen, Valerie Malyvanh. "Generation and characterization of a knock-in allele of EKLF probing the in vivo role of the chromatin remodeling domain in definitive hematopoietic cells /." View the abstract View the abstract Download the full-text PDF version Download the full-text PDF version, 2009. http://etd.uthsc.edu/ABSTRACTS/2009-025-Jansen-index.htm.
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Thesis (Ph.D.)--University of Tennessee Health Science Center, 2009.Title from title page screen (viewed on February 4, 2010). Research advisor: John M. Cunningham, M.D. Document formatted into pages (xiv, 115 p. : ill.). Vita. Abstract. Includes bibliographical references (p. 89-103).
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Hallal, Samantha. "Characterisation of the zinc fingers of Erythroid Kruppel-Like Factor." University of Sydney, 2008. http://hdl.handle.net/2123/4030.
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Doctor of Philosophy (PhD)Gene expression is known to be regulated at the level of transcription. Recently, however, there has been a growing realisation of the importance of gene regulation at the post-transcriptional level, namely at the level of pre-mRNA processing (5’ capping, splicing and polyadenylation), nuclear export, mRNA localisation and translation. Erythroid krüppel-like factor (Eklf) is the founding member of the Krüppel-like factor (Klf) family of transcription factors and plays an important role in erythropoiesis. In addition to its nuclear presence, Eklf was recently found to localise to the cytoplasm and this observation prompted us to examine whether this protein has a role as an RNA-binding protein, in addition to its well-characterised DNA-binding function. In this thesis we demonstrate that Eklf displays RNA-binding activity in an in vitro and in vivo context through the use of its classical zinc finger (ZF) domains. Furthermore, using two independent in vitro assays, we show that Eklf has a preference for A and U RNA homoribopolymers. These results represent the first description of RNA-binding by a member of the Klf family. We developed a dominant negative mutant of Eklf by expressing its ZF region in murine erythroleukaemia (MEL) cells. We used this to investigate the importance of this protein in haematopoietic lineage decisions by examining its effect on the multipotent K562 cell line. We provide evidence that Eklf appears to be critical not only for the promotion of erythropoiesis, but also for the inhibition of megakaryopoiesis.
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Canadas, Maria Belen. "Cooperative EKF localization." [S.l. : s.n.], 2004. http://www.bsz-bw.de/cgi-bin/xvms.cgi?SWB11730075.
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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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Turunc, Cagri. "An Implementation Of Ekf Slam With Planar Segments." Master's thesis, METU, 2012. http://etd.lib.metu.edu.tr/upload/12614906/index.pdf.
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Localization and mapping are vital capabilities for a mobile robot. These two capabilities strongly depend on each other and simultaneously executing both of these operations is called SLAM (Simultaneous Localization and Mapping). SLAM
problem requires the environment to be represented with an abstract mapping model. It is possible to construct a map from point cloud of environment via scanner sensor systems. On the other hand, extracting higher level of features from
point clouds and using these extracted features as an input for mapping system is also a possible solution for SLAM.
In this work, a 4D feature based EKF SLAM system is constructed and open form of equations of algorithm are presented. The algorithm is able to use center of mass
and direction of features as input parameters and executes EKF SLAM via these parameters. Performance of 4D feature based EKF SLAM was examined and compared with 3D EKF SLAM via monte-carlo simulations. By this wayit is believed that, contribution of adding a direction vector to 3D features is investigated and illustrated via graphs of monte-carlo simulations. At the second part of the work, a scanner sensor system with IR distance finder is designed and constructed. An algorithm was presented to extract planar features from data collected by sensor system. A noise model was proposed for output features of sensor and 4D EKF SLAM algorithm was executed via extracted features of scanner system. By this way, performance of 4D EKF SLAM algorithm is tested with real sensor data and output results are compared with 3D features. So in this work, contribution of using 4D features instead of 3D ones was examined via comparing performance of 3D and 4D algorithms with simulation results and real sensor data.
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Tønne, Karianne Knutsen. "Stability Analysis of EKF - based Attitude Determination and Control." Thesis, Norwegian University of Science and Technology, Department of Engineering Cybernetics, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8720.
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This thesis is a part of the SSETI (Student Space Exploration Technology Initiative) project, where students from several universities around Europe work together with the European Space Agency (ESA) with designing, building, testing and launching an Earth-Moon satellite orbiter (European Student Moon Orbiter (ESMO). A satellite model with reaction wheels placed in tetrahedron was deduced in a preliminary study together with an extended Kalman filter to estimate the attitude from star measurements. The stability and convergence properties of this system are studied in this thesis. Previous studies on the convergence of extended Kalman filter are presented and a proof of exponentially convergence of a system with extended Kalman filter is given and used to prove that ESMO with the extended Kalman filter converges exponentially. The most recent work and different methods to apply a nonlinear separation principle is presented. Three feedback controllers with proof of global asymptotic stability (GAS) is then introduced and implemented on ESMO. Based upon the global asymptotic stability of the feedback controllers, and the proof that the extended Kalman filter works as an exponentially observer, a nonlinear separation principle is deduced. The closed loop system can then be stated globally asymptotically stable based upon the deduced separation principle. The closed loop with the three different controllers is then simulated in Simulink for varying gains and different reference steps. The three controllers show stable characteristic as the theory implies. The robust controller shows best tracking and estimation properties, it is very accurate, simple, robust and adaptable to environmentally changes, and is therefore proposed as the most suitable controller for ESMO.
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LUNDGREN, DOUGLAS. "A study of EKF-SLAM for vehicle state estimation." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-263289.
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Self driving vehicles consists of advanced Commercial-Off-The-Shelf components which are developed separately by different manufacturers and can introduce uncertainties to the system. To guarantee that the system meets the safety requirements is a complex task and it is important to test the performance of components beforehand. This paper examines a state estimation technique called EKF-SLAM and the aim of the research is to test the algorithm against erroneous sources and discuss its robustness. It is tested in a simulated environment called preScan against noise from sensors, timing delays and dynamic objects. In conclusion the performance of EKF seem to be limited and is dependent on a highly accurate feature extraction method. It showed to be robust against large sensor noise but for small errors a simpler method called odometry seemed much more preferable. Since EKF is derived from the assumption that all landmarks are static it became a complex task to cope with dynamic objects, and with network delays the results show quite clearly that some compensations are needed.Självkörande fordon består av avancerade Commercial-Off-The-Shelf komponenter som utvecklas separat och introducerar osäkerheter i systemet. Att garantera att systemet ska uppfylla alla säkerhetskrav är en komplicerad uppgift och det är därför viktigt att granska komponenternas prestanda i förhand. Den här rapporten undersöker en SLAM algorithm som kallas EKF och syftet med undersökning är att testa algoritmen mot felaktiga källor och diskutera dess robusthet. Algortihmen testas i en trafiksimulator som kallas preScan och testas mot oljud i hastighetsmätaren, rörliga objekt och tidsfördröjningar i ett distribuerat nätverk. Sammanfattningsvis så finns det klara begränsningar i hur bra EKF kan prestera och är beroende av en bra sensor model. Algoritmen visade sig vara robust mot stora fel i hastighetsmätaren, men för små fel tycks en enkel distansmätnings-metod vara mycket mer föredragen. Eftersom EKF härleds från antagandet att alla objekt är statiska så blev det en komplex uppgift att hantera dynamiska objekt. Tidsfördröjningar i nätverket påverkar resultaten tydligt och visar på att kompensationer behöver göras.
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Tüysüz, Mustafa. "Fikir ve sanat eserleri kanunu çerçevesinde fikri haklar üzerindeki sözleşmeler : (ilgili mevzuat ekli) /." Ankara : Yetkin, 2007. http://swbplus.bsz-bw.de/bsz273054511inh.htm.
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Mustefa, Dinsefa, and Ermias Mebreku. "OFDM Systems Offset Estimation and Cancellation Using UKF and EKF." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-15524.
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Orthogonal Frequency Division Multiplexing (OFDM) is an efficient multi- carrier modulation scheme, which has been adopted for several wireless stan- dards. Systems employing this scheme at the physical layer are sensitive to frequency offsets and that causes Inter Carrier Interference (ICI) and degra- dation in overall system performance of OFDM systems. In this thesis work, an investigation on impairments of OFDM systems will be carried out. Anal- ysis of previous schemes for cancellation of the ICI will be done and a scheme for estimating and compensating the frequency offset based on Unscented Ka- man Filter (UKF) and Extended Kaman Filter (EKF) will be implemented. Analysis on how the UKF improves the Signal to Noise Ratio (SNR); and how well it tracks the frequency offset estimation under Additive White Gaussian Noise (AWGN) channel and flat fading Rayleigh channel will be carried on.
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Huo, Jin. "Build and evaluate state estimation Models using EKF and UKF." Thesis, KTH, Fordonsdynamik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-140947.
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In vehicle control practice, there are some variables, such as lateral tire force, body slip angle and yaw rate, that cannot or is hard to be measured directly and accurately. Vehicle model, like the bicycle model, offers an alternative way to get them indirectly, however due to the widely existent simplification and inaccuracy of vehicle models, there are always biases and errors in prediction from them. When developing advanced vehicle control functions, it is necessary and significant to know these variables in relatively high precision. Kalman filter offers a choice to estimate these variables accurately with measurable variables and with vehicle model together. In this thesis, estimation models based on Extended Kalman Filter (EKF) and Uncented Kalman Filter (UKF) are built separately to evaluate the lateral tire force, body slip angel and yaw rate of two typical passenger vehicles. Matlab toolbox EKF/UKF developed by Simo Särkkä, et al. is used to implement the estimation models. By comparing their principle, algorithm and results, the better one for vehicle state estimation will be chosen and justified. The thesis is organized in the following 4 parts: First, EKF and UKF are studied from their theory and features. Second, vehicle model used for prediction in Kalman filter is build and justified. Third, algorithms of EKF and UKF for this specific case are analysed. EKF and UKF are then implemented based on the algorithms with the help of Matlab toolbox EKF/UKF. Finally, comparisons between EKF and UKF are presented and discussed.
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Sanchez-Rey, Roberto. "EKF-based parameter estimation for MPC applied to lateral vehicle dynamics." Thesis, KTH, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-217296.
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Research about model-based techniques (MPC) applications for automotive drivingis nowadays experiencing an increasing interest thanks to improvements on hardwarecapabilities. Those allow for computationally heavy techniques as non-linear MPC is.MPC-based techniques rely on models of the controlled plant for predicting future state-space vectors. Such models are either not accurate enough for the purpose or affected byuncertainties, hence potentially causing a lack of robustness. By updating the predictionsmodel in run-time, learning-based MPC techniques are theoretically suitable to increasemodel/plant resemblance hence system performance.This thesis is focused in the study of application of a learning technique when theuncertainty of the model is of type parametric, i.e., the structure and the order of themodel is known, but some relevant parameters are uncertain or varying. Such a learn-ing technique is based on a recursion of the well-known extended Kalman filter, thatmakes use of the measured (noiseless) states for estimating the unknown parameters ofthe model.The study is performed on a Matlab simulation environment where the lateral dy-namics of a ground vehicle are controlled. For the purpose, different trajectories areused as references for a simulated vehicle that is intended to keep a minimum lateraldeviation from them. Two different models are tested: kinematic and dynamic. For thefirst case, one parameter is estimated:the distance between the rear axis and the center ofgravity of the vehicle. The dynamic model has two uncertain parameters, correspondingto cornering stiffness coefficients of both front and rear tyres. Those are depending onroad-vehicle friction coefficient, hence could resemble an unknown road condition case.Simulation results proved that at the cost of an increased computational time, it ispossible to improve the lateral dynamics as consequence of a better estimation value forthe uncertain parameter.Intresset för forskning på tillämpningar av modellbaserade tekniker för autonomkörning är på uppgång tack vare ökad hårdvarukapacitet. Den ökade kapaciteten gördet möjligt att använda beräkningsmässigt krävande tekniker såsom olinjär MPC. MPC-baserade tekniker bygger på att en modell av systemet används för att beräkna framtidatillståndsvektorer. Sådana modeller kan emellertid vara alltför inexakta för den tänktatillämpningen eller vara under påverkan av osäkerheter, vilket kan leda till brist på ro-busthet. Genom att uppdatera modellen under körning, kan modellen förbättras ochdärmed även systemets perstanda. Ur ett teoretiskt persektiv är inlärnigsbaserade MPC-tekniker väl lämpade för detta ändamål.I detta arbete ligger fokus på att studera tillämpningar av en inlärningsteknik närosäkerheten i modellen är parametrisk, d.v.s. när modellens struktur är känd men detfinns osäkerhet i parametrarnas värden. En sådan inlärningsteknik bygger på rekursionav det välkända utökade Kalmanfiltret, i vilket uppmätta tillstånd (fria från mätbrus)används till att uppskatta de okända parametrarna i modellen.Studien genomfördes i en simuleringsmiljö i Matlab, i vilken den laterala dynamikenhos fordonet kan kontrolleras. För detta ändamål, användes flera olika referensbanor tillvilka det simulerade fordonet skulle hålla så liten lateral avvikelse från som möjligt. Idet första fallet uppskattas endast en parameter, nämligen avståndet mellan den bakreaxeln och fordonets tyngdpunkt. I den dynamiska modellen uppskattas däremot tvåparametrar; styvhetskoefficienterna för både bakre och främre däck. Dessa beror frik-tionskoefficient mellan väg och fordon och kan därför ses som ett fall i vilket underlagetär okänt.Simuleringsresultat visar på att det är möjligt att förbättra den laterala dynamiken,som följd av bättre uppskattning av den okända parameterns värde, men att detta skertill priset av ökad beräkningstid.
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Henrik, Fåhraeus. "Fusion of IMU and Monocular-SLAM in a Loosely Coupled EKF." Thesis, Linköpings universitet, Reglerteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-136910.
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Camera based navigation is getting more and more popular and is the often the cornerstone in Augmented and Virtual Reality. However, navigation systems using camera are less accurate during fast movements and the systems are often resource intensive in terms of CPU and battery consumption. Also, the image processing algorithms introduce latencies in the systems, causing the information of the current position to be delayed. This thesis investigates if a camera and an IMU can be fused in a loosely coupled Extended Kalman Filter to reduce these problems. An IMU introduces unnoticeable latencies and the performance of the IMU is not affected by fast movements. For accurate tracking using an IMU it is important to estimate the bias correctly. Thus, a new method was used in a calibration step to see if it could improve the result. Also, a method to estimate the relative position and orientation between the camera and IMU is evaluated. The filter shows promising results estimating the orientation. The filter can estimate the orientation without latencies and can also offer accurate tracking during fast rotation when the camera is not able to estimate the orientation. However, the position is much harder and no performance gain could be seen. Some methods that are likely to improve the tracking are discussed and suggested as future work.
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HUGMARK, JOAKIM. "Inertial-aided EKF-based Structure from Motionfor Robust Real-time Augmented Reality." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-142344.
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The aim of this project was to develop a system that enables the overlay of computer graphics in a video sequence recorded by a moving camera. To do this, the camera’s position relative different landmarks in the picture needs to be estimated, a problem commonly referred to as “Structure from Motion” within the Computer Vision community, or “Monocular SLAM” within the robotics community. The system should be robust in the sense that it can handle partial occlusions and dynamic environments, as well as large open spaces without much texture in the image. To enable this, an IMU-unit is used to complement the visual input from the camera. The system is based on previous work by Civera et al. and uses the Extended Kalman Filter (EKF) to fuse the sensor inputs. A 1-point RANSAC method is used to efficiently detect and discard outliers in the sensor data. Inverse depth parameterization is used to enable the use of landmarks far away from the camera. The IMU used in the project is developed by X-IO Technologies, and uses an on-board algorithm developed by Madgwick to determine a precise and drift-free orientation. The system is evaluated using multiple video sequences recorded in a setting similar to where the system is intended to be used. Results indicate that the IMU really helps the system to differentiate ambiguities between translational and rotational movements, as well as keeping the system stable during smaller occlusions. Some cases where the system often fails are also identified. The performance of the system is evaluated, as well as how some differMålet med detta projekt var att utveckla ett system som möjliggör placerandet av datorgrafik i en videosekvens inspelad av en kamera i rörelse. För att åstadkomma detta måste kamerans position och rotation relativt landmärken i dess omgivande miljö uppskattas. Detta problem benämns ofta ”Structure from Motion” (inom datorseendeområdet) eller ”Monocular SLAM” (inom robotikområdet). Systemet ska även vara robust så tillvida att det kan hantera partiella ocklusioner och icke-statiska miljöer, liksom att operera i stora öppna områden vilket resulterar i att bilden kan innehålla väldigt lite texturer. För att kunna uppnå detta används en IMU-enhet som komplement till den visuella information som kameran ger. Systemet är baserat på tidigare forskning av framförallt Civera et al. och använder ett Extended Kalmanfilter (EKF) för att sammanfoga de olika sensorernas data. En 1-punkts RANSAC-metod används för att detektera och avfärda outliers i sensordatan. Invers djupparameterisering används för att möjliggöra användandet av avlägsna landmärken. IMUn som används är utvecklad av X-IO Technologies och använder en integrerad algoritm utvecklad av Madgwick för att beräkna en exakt och driftfri absolut orientering. Systemet utvärderas med flera videosekvenser inspelade i en miljö som liknar den där det är tänkt att användas. Resultaten antyder att IMUn verkligen hjälper systemet att särskilja tvetydigheter gällande translationer och rotationer, samt hjälper till att hålla systemet stabilt vid mindre ocklusioner. En del fall där systemet ofta misslyckas är också identifierade. Systemets prestanda utvärderas, liksom hur flera av dess parametrar påverkar resultatet.
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Wahlqvist, Kristian. "A Comparison of Motion Priors for EKF-SLAM in Autonomous Race Cars." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-262673.
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Simultaneous Localization and Mapping (SLAM) is one of the fundamental problems to solve for any autonomous vehicle or robot. The SLAM problem is for an agent to incrementally build a map of its surroundings while keeping track of its location within the map, using various sensors. The goal of this thesis is to demonstrate the differences and limitations of different odometry methods when used as the prior motion estimate for the common SLAM algorithm EKF-SLAM. Inspired by autonomous racing, the algorithms were evaluated for especially difficult driving scenarios such as high velocities and while the car was skidding. Three different odometry algorithms that all rely on different sensors were implemented; The feature based stereo visual odometry algorithm Libviso2, the lidar odometry algorithm Range Flow-based 2D Odometry (RF2O), and wheeled odometry fused with measurements of the vehicles angular velocity from a gyroscope. The different algorithms were evaluated separately on real data that was gathered by running a modified RC car equipped with the necessary sensors around different racing track configurations. The car was driven for different levels of aggressiveness, where more aggressive driving implies a higher velocity and skidding. The SLAM estimate of the vehicle position and cone locations were evaluated in terms of mean absolute error (MAE) and computational time, for all motion priors separately on each track. The results show that Libviso2 provides an accurate prior motion estimate with consistent performance over all test cases. RF2O and the wheeled odometry approach could in some of the cases provide a prior motion estimate that was sufficient for accurate SLAM performance, but performed poorly for other cases.Simultan lokalisering och kartläggning (SLAM) är ett grundläggande problem att lösa för alla typer av autonoma fordon eller robotar. SLAM handlar om problemet för en agent att inkrementellt konstruera en karta av sin omgivning samtidigt som den håller koll på sin position inom kartan, med hjälp av diverse sensorer. Målet med detta examensarbete är att demonstrera skillnader och begränsningar för olika metoder att uppskatta bilens momentana förflyttning, när denna momentana förflyttning används som en fösta skattning av bilens rörelse för SLAM-algoritmen EKF-SLAM. Utvärderingen grundar sig i autonom motorsport och de undersökta algoritmerna utvärderades under speciellt svåra förhållanden så som hög hastighet och när bilen sladdar. Tre olika metoder för att skatta bilens momentana förflyttning implementerades där samtliga metoder baseras på data från olika sensorer. Dessa var den visuella metoden Libviso2 som använder stereokameror, den flödesbaserade metoden RF2O som använder en 2D lidar, samt en metod som baseras på hjulens rotationshastighet som kombinerades med fordonets uppmätta vinkelhastighet från ett gyroskop. De olika algoritmerna utvärderades separat på data som genererats genom att köra en modifierad radiostyrd bil runt olika banor utmarkerade av trafikkoner, samt för olika nivåer av aggressiv körstil. Estimeringen av bilens bana och konernas positioner jämfördes sedan separat i termer av medelabsolutfel samt beräkningstid för varje metod och bana. Resultaten visar att Libviso2 ger en bra skattning av den momentana förflyttningen och presterar konsekvent över samtliga tester. RF2O och metoden baserad på hjulens rotationshastighet var i vissa fall tillräckligt bra för korrekt lokalisering och kartläggning, men presterade dåligt i andra fall.
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Júnior, Celso Olivete. "Estimativa da idade óssea através da análise carpal baseada na simplificação do método de Eklof & Ringertz." Universidade de São Paulo, 2005. http://www.teses.usp.br/teses/disponiveis/18/18133/tde-15012010-113524/.
Full textAbstract:
Este trabalho apresenta uma metodologia semi-automática e simplificada para estimação da idade óssea baseada no método de Eklof & Ringertz. Fundamenta-se no processamento e extração de informações de imagens radiográficas da mão, mostrando a real influência do efeito Heel nesse tipo de imagem. Apresenta resultados obtidos com a aplicação de algoritmos de thresholding, com e sem a correção do efeito Heel,e propõe uma metodologia para isolar os ossos do tecido da mão para obtenção de dimensões dos mesmos. Essas dimensões foram usadas como informações para a estimação da idade óssea de seres humanos em fase de crescimento, buscando uma simplificação do método de Eklof & Ringertz.This research presents a semi-automatic and simplified methodology to estimate skeletal age by using the Eklof & Ringertz method. The method is based on processing and extraction of hand radiographic images information, showing the Heel effect real influence in this type of image. The results obtained with application of thresholding algorithms, with and without Heel effect correction are presented and a methodology to isolate bone from hands tissue for dimension measurements is proposed. These dimensions were used as the information for skeletal age estimation of humans in the growth phase, on order to simplify the Eklof & Ringertz method.
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Iessa, Hassan. "Concentrating food materials using electrokinetically enhanced filtration (EKEF) : impact on filtration efficiency, quality compounds and power consumption." Thesis, University of Newcastle upon Tyne, 2016. http://hdl.handle.net/10443/3483.
Full textAbstract:
Countries around the world are suffering from many problems caused by the misuse of their resources either by using non-renewable ones or destroying the valuable features of the resources that are available. Some of the most important challenges facing humanity are related to food and energy security. Food security includes many aspects besides providing people with an adequate amount of safe and healthy food; it also includes improving food processing to produce acceptable, high quality food products with reduced losses of nutrients, carbon footprint and using fewer resources in the production/packaging processes. Dried and concentrated foods have become an important category in the food products marketplace. Many of the usual traditional methods require very high energy inputs. These methods may also have significant negative impacts on the foodstuffs, especially their chemical properties, such as loss of essential nutrients, as well as their appearance, aromas and flavour. This project aimed to combine the electrokinetic phenomenon of electro-osmosis with the traditional functions of filtration to form a process of electrokinetically enhanced filtration (EKEF). This process is based on using electrokinetic effects to enhance conventional filtration technologies to concentrate foodstuffs at low temperature which saves energy, time, and product constituents associated with product quality, especially the temperature-sensitive components such as Vitamin C, fruit/vegetable colours and key aromas. This study is concerned with factors affecting the efficiency of the EKEF process after its application to concentrate orange juice (14% dry matter content, pH=3.6) and malt extract (20% dry matter content, pH=4.55), and monitoring the changes that occur in indicators of food quality, such as the change in pH value, vitamin C content, colour, dry matter as well as the energy inputs to facilitate the EKEF. The process of electrokinetics was combined with micro-filtration (MF) to speed up the process and save time and energy. This is carried out in an EKEF-rig which was designed to meet the project needs with an ability to change all the dependent parameters to find the optimum conditions to run this application. Applying EKEF improved the filtration process and achieved net dewatering efficiencies up to 7.43% and 4.86% for orange juice and 10.68% and 6.26% for malt extract after 5.5hrs of processing under 30V and 15V respectively; and increased dry matter content from 14% to ii 33.09% and 29.52% for orange juice and from 20% to 34.2% and 32.14% for malt extract after 5.5hrs under 30V and 15V respectively. The maximum dry matter content was 28.75% and 29.63% under the control condition after the same time for orange juice and malt extract respectively. Results showed that the performance of the EKEF process was dependent upon several parameters such as the sample’s conductivity, applied voltage used, applied pressure, initial sample’s thickness (the distance between electrodes) and filters pore size; all of these factors had a positive impact on improving the process efficiency except of the initial sample’s thickness which had a negative impact. Regarding the impact on the heat-sensitive food compounds, here represented by vitamin C; applying EKEF for 5.5hours reduced vitamin C retention in the concentrated juice to 41% under 15V and 7% under 30V. The retention of the total filtrate was also influenced by the EKEF process, where it fell to 48% under 15V and 35% under 30V. Vitamin C retention was better under the EKEF process compared with its value under conventional thermal methods, where it needed about 10-15min at 50-75ºC and (3min) at 90ºC to drop to 50% or less (Vikram et al., 2005). EKEF also had a negative impact on the juice colour and pH value. The direction of water flow and the profile of fluid flow between the two electrodes were thoroughly investigated to identify the main filtration outlet and its position; the power consumption of the application was also calculated and compared with power consumption of conventional thermal methods. The EKEF process resulted in a large power consumption addition compared with applying the pressure only; however, the required additional energy needed to achieve the same increase in dewatering efficiency by increasing the pressure only without EK was relatively small. On the other hand, EKEF showed a good efficiency in energy saving compared with the power consumption required in more conventional thermal methods used to evaporate an equal amount of water. This saving was up to 18.35 times and 47.34 times in OJ and ME dewatering respectively.
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Tamagnini, Filippo. "EKF based State Estimation in a CFI Copolymerization Reactor including Polymer Quality Information." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20235/.
Full textAbstract:
State estimation is an integral part of modern control techniques, as it allows to characterize the state information of complex plants based on a limited number of measurements and the knowledge of the process model. The benefit is twofold: on one hand it has the potential to rationalize the number of measurements required to monitor the plant, thus reducing costs, on the other hand it enables to extract information about variables that have an effect on the system but would otherwise be inaccessible to direct measurement.
The scope of this thesis is to design a state estimator for a tubular copolymerization reactor, with the aim to provide the full state information of the plant and to characterize the quality of the product.
Due to the fact that, with the existing set of measurements, only a small number of state variables can be observed, a new differential pressure sensor is installed in the plant to provide the missing information, and a model for the pressure measurement is developed.
Following, the state estimation problem is approached rigorously and a comprehensive method for analyzing, tuning and implementing the state estimator is assembled from scientific literature, using a variety of tools from graph theory, linear observability theory and matrix algebra. Data reduction and visualization techniques are also employed to make sense of high dimensional information.
The proposed method is then tested in simulations to assess the effect of the tuning parameters and measured set on the estimator performance during initialization and in case of estimation with plant-model mismatch.
Finally, the state estimator is tested with plant data.
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Dey, Rohit. "MonoDepth-vSLAM: A Visual EKF-SLAM using Optical Flow and Monocular Depth Estimation." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627666226301079.
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Tonel, Giovani. "Avaliação dos estimadores EKF, CEKF e MHE para aplicações em linha em processos." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2008. http://hdl.handle.net/10183/13469.
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Devido aos constantes avanços computacionais, bem como o desenvolvimento de eficientes métodos para a solução de problemas de otimização não-lineares, tem-se tornado interessante a realização de otimização em tempo real e como conseqüência o uso de estimadores on-line em processos químicos não lineares. Neste sentido, a atualização automática de modelos de processos torna-se interessante permitindo a realização de estimativas em tempo real de variáveis infreqüentemente medidas e/ou imensuráveis e de variáveis estados e parâmetros desconhecidos que são variantes no tempo. Usualmente, a atualização automática de modelos é feita baseado em algumas variáveis secundárias que são medidas on-line, como temperatura, pressão, composição e vazão. Nos estimadores baseados no filtro de Kalman, como o EKF e CEKF, os esforços computacionais são relativamente pequenos, variando de um simples cálculo algébrico de um ganho, por exemplo, o EKF, até a resolução de problema de otimização quadrático, como exemplo o CEKF. Estes pequenos esforços computacionais permitem rápidos resultados com relativa acuracidade, mas estes estimadores baseados no filtro de Kalman podem falhar quando o sistema tem acentuada não-linearidade, por exemplo. De outra maneira, a formulação MHE é capaz de tratar uma vasta gama de sistemas não-lineares, como aqueles que têm inversão do sinal de ganho de acordo com o ponto de operação. No entanto, na formulação MHE tem-se a necessidade de se resolver um problema NLP não convexo com muitas equações de desigualdade e graus de liberdade, e como conseqüência o tempo de processamento torna-se maior que o tempo de amostragem, fazendo impraticável a execução de ações de controle sobre o sistema em tempo real. Assim, é necessário a implementação de eficientes técnicas para resolver de maneira rápida os problemas de otimização dinâmica envolvidos na formulação MHE. Neste trabalho as estratégias seqüencial e simultânea são exploradas, almejando-se a aceleração da solução dos estágios de integração e otimização dinâmica do estimador MHE, permitindo uma ampla avaliação entre o estimador MHE e os estimadores baseados no filtro de Kalman. Os estimadores foram aplicados para sete estudos de caso, como a planta de quatro tanques cilíndricos, o modelo do reator isotérmico com a reação de van de Vusse e o modelo de um CSTR exotérmico instável. A partir dos resultados, as vantagens e desvantagens da formulação via horizonte móvel são discutidas de modo a justificar o elevado esforço empregado na avaliação e projeto deste, comparado com os estimadores EKF e CEKF, quando o sistema tem acentuada não linearidade, incertezas no modelo e distúrbios e/ou ruídos nas medições.Due to the constant computational advances, as well as the development of efficient methods for solving nonlinear optimization problems, it has become interesting to carry out dynamic optimizations in real time and the consequent use of on-line estimators on nonlinear chemical processes. In this framework, the automatic updating of process models becomes attractive to allow the accomplishment of real-time estimates of unmeasured or infrequent-measured variables, states variables and unknown or time-variant model parameters. Usually, the process model updating is made based on some auxiliary variables that are measured on-line, such as temperature, pressure, composition, and flow rate. In the traditional Kalman filter-based estimators, such as EKF and CEKF, the computational efforts are relatively small, ranging from a simple algebraic calculation of a gain, as in the EKF case, to the resolution of a quadratic optimization problem, like in the CEKF case. These small computational efforts allow faster results with relative accuracy, but these Kalman filter-based estimators may fail when the system has meaningful nonlinearities. On the other hand, the MHE formulation is able to treat a large range of nonlinear systems, as those that have the gain sign inversion according to the operating point. However, it has the necessity to solve possible non-convex NLP problem with many inequality equations and degrees of freedom, and as consequence, the processing time may become larger than the sampling time, making impracticable the execution of the control actions over the system in real time. Thus, it is necessary to implement efficient techniques to solve in a fast way the integration and dynamic optimization problems embedded in the MHE formulation. In this work, sequential and simultaneous strategies are explored to solve the estimators, aiming to speed up the solution of the integration and dynamic optimization stages, allowing a comprehensive evaluation between MHE and Kalman filter-based estimators. The estimators were applied to seven case studies, like the quadruple tank system, van de Vusse isothermal CSTR model and a more non-linear and unstable exothermal CSTR model. From the results, the advantages and drawbacks of the moving horizon formulation are brought up to justify the high effort spent in the design and evaluation phases, compared to the EKF and CEKF estimators, when the system has relatively high nonlinearities, model uncertainties and measurement disturbances.
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Pereira, Savio Joseph. "On the utilization of Simultaneous Localization and Mapping(SLAM) along with vehicle dynamics in Mobile Road Mapping Systems." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/94425.
Full textAbstract:
Mobile Road Mapping Systems (MRMS) are the current solution to the growing demand for high definition road surface maps in wide ranging applications from pavement management to autonomous vehicle testing. The focus of this research work is to improve the accuracy of MRMS by using the principles of Simultaneous Localization and Mapping (SLAM). First a framework for describing the sensor measurement models in MRMS is developed. Next the problem of estimating the road surface from the set of sensor measurements is formulated as a SLAM problem and two approaches are proposed to solve the formulated problem. The first is an incremental solution wherein sensor measurements are processed in sequence using an Extended Kalman Filter (EKF). The second is a post-processing solution wherein the SLAM problem is formulated as an inference problem over a factor graph and existing factor graph SLAM techniques are used to solve the problem. For the mobile road mapping problem, the road surface being measured is one the primary inputs to the dynamics of the MRMS. Hence, concurrent to the main objective this work also investigates the use of the dynamics of the host vehicle of the system to improve the accuracy of the MRMS. Finally a novel method that builds off the concepts of the popular model fitting algorithm, Random Sampling and Consensus (RANSAC), is developed in order to identify outliers in road surface measurements and estimate the road elevations at grid nodes using these measurements. The developed methods are validated in a simulated environment and the results demonstrate a significant improvement in the accuracy of MRMS over current state-of-the art methods.Doctor of Philosophy
Mobile Road Mapping Systems (MRMS) are the current solution to the growing demand for high definition road surface maps in wide ranging applications from pavement management to autonomous vehicle testing. The objective of this research work is to improve the accuracy of MRMS by investigating methods to improve the sensor data fusion process. The main focus of this work is to apply the principles from the field of Simultaneous Localization and Mapping (SLAM) in order to improve the accuracy of MRMS. The concept of SLAM has been successfully applied to the field of mobile robot navigation and thus the motivation of this work is to investigate its application to the problem of mobile road mapping. For the mobile road mapping problem, the road surface being measured is one the primary inputs to the dynamics of the MRMS. Hence this work also investigates whether knowledge regarding the dynamics of the system can be used to improve the accuracy. Also developed as part of this work is a novel method for identifying outliers in road surface datasets and estimating elevations at road surface grid nodes. The developed methods are validated in a simulated environment and the results demonstrate a significant improvement in the accuracy of MRMS over current state-of-the-art methods.
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Shytermeja, Enik. "Design and performance of a GNSS single-frequency multi-constellation vector tracking architecture for urban environments." Phd thesis, Toulouse, INPT, 2017. http://oatao.univ-toulouse.fr/19756/1/SHYTERMEJA_Enik.pdf.
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In the last decade, Global Navigation Satellites Systems (GNSS) have gained a significant position in the development of urban navigation applications and associated services. The urban environment presents several challenges to GNSS signal reception, such as multipath and GNSS Line-of-Sight (LOS) blockage, which are translated in the positioning domain in a decreased navigation solution accuracy up to the lack of an available position. For this matter, Vector Tracking (VT) constitutes a promising approach able to cope with the urban environment-induced effects including multipath, NLOS reception and signal outages. This thesis is particularly focused on the proposal and design of a dual constellation GPS + Galileo single frequency L1/E1 Vector Delay Frequency Lock Loop (VDFLL) architecture for the automotive usage in urban environment. From the navigation point of view, VDFLL represents a concrete application of information fusion, since all the satellite tracking channels are jointly tracked and controlled by the common navigation Extended Kalman filter (EKF). The choice of the dual-constellation single frequency vector tracking architecture ensures an increased number of observations and at the same time allowing the conservation of the low-cost feasibility criteria of the mobile user’s receiver. Moreover, the use of single frequency L1 band signals implies the necessity of taking into account the ionospheric error effect. In fact, even after the application of the ionosphere error correction models, a resultant ionospheric residual error still remains in the received observations. The originality of this work relies on the implementation of a dual-constellation VDFLL architecture, capable of estimating the ionosphere residual error present in the received observations. This dissertation investigates the VDFLL superiority w.r.t the scalar tracking receiver in terms of positioning performance and tracking robustness for a real car trajectory in urban area in the presence of multipath and ionosphere residual error.
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Leven, William Franklin. "Approximate Cramer-Rao Bounds for Multiple Target Tracking." Diss., Georgia Institute of Technology, 2006. http://hdl.handle.net/1853/10507.
Full textAbstract:
The main objective of this dissertation is to develop mean-squared error performance predictions for multiple target tracking. Envisioned as an approximate Cramer-Rao lower bound, these performance predictions allow a tracking system designer to
quickly and efficiently predict the general performance trends of a tracking system.
The symmetric measurement equation (SME) approach to multiple target tracking
(MTT) lies at the heart of our method. The SME approach, developed by Kamen
et al., offers a unique solution to the data association problem. Rather than deal directly with this problem, the SME approach transforms it into a nonlinear estimation
problem. In this way, the SME approach sidesteps report-to-track associations.
Developing performance predictions using the SME approach requires work in several areas: (1) extending SME tracking theory, (2) developing nonlinear filters for SME tracking, and (3) understanding techniques for computing Cramer-Rao error bounds in nonlinear filtering. First, on the SME front, we extend SME tracking theory by deriving a new set of SME equations for motion in two dimensions. We also develop
the first realistic and efficient method for SME tracking in three dimensions. Second,
we apply, for the first time, the unscented Kalman filter (UKF) and the particle filter
to SME tracking. Using Taylor series analysis, we show how different SME implementations affect the performance of the EKF and UKF and show how Kalman filtering degrades for the SME approach as the number of targets rises. Third, we explore the Cramer-Rao lower bound (CRLB) and the posterior Cramer-Rao lower bound (PCRB)
for computing MTT error predictions using the SME. We show how to compute performance predictions for multiple target tracking using the PCRB, as well as address confusion in the tracking community about the proper interpretation of the PCRB for tracking scenarios.
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Papakis, Ioannis. "A Bayesian Framework for Multi-Stage Robot, Map and Target Localization." Thesis, Virginia Tech, 2019. http://hdl.handle.net/10919/93024.
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This thesis presents a generalized Bayesian framework for a mobile robot to localize itself and a target, while building a map of the environment. The proposed technique builds upon the Bayesian Simultaneous Robot Localization and Mapping (SLAM) method, to allow the robot to localize itself and the environment using map features or landmarks in close proximity. The target feature is distinguished from the rest of features since the robot has to navigate to its location and thus needs to be observed from a long distance. The contribution of the proposed approach is on enabling the robot to track a target object or region, using a multi-stage technique. In the first stage, the target state is corrected sequentially to the robot correction in the Recursive Bayesian Estimation. In the second stage, with the target being closer, the target state is corrected simultaneously with the robot and the landmarks. The process allows the robot's state uncertainty to be propagated into the estimated target's state, bridging the gap between tracking only methods where the target is estimated assuming known observer state and SLAM methods where only landmarks are considered. When the robot is located far, the sequential stage is efficient in tracking the target position while maintaining an accurate robot state using close only features. Also, target belief is always maintained in comparison to temporary tracking methods such as image-tracking. When the robot is closer to the target and most of its field of view is covered by the target, it is shown that simultaneous correction needs to be used in order to minimize robot, target and map entropies in the absence of other landmarks.M.S.
This thesis presents a generalized framework with the goal of allowing a robot to localize itself and a static target, while building a map of the environment. This map is used as in the Simultaneous Localization and Mapping (SLAM) framework to enhance robot accuracy and with close features. Target, here, is distinguished from the rest of features since the robot has to navigate to its location and thus needs to be continuously observed from a long distance. The contribution of the proposed approach is on enabling the robot to track a target object or region, using a multi-stage technique. In the first stage, the robot and close landmarks are estimated simultaneously and they are both corrected. Using the robot's uncertainty in its estimate, the target state is then estimated sequentially, considering known robot state. That decouples the target estimation from the rest of the process. In the second stage, with the target being closer, target, robot and landmarks are estimated simultaneously. When the robot is located far, the sequential stage is efficient in tracking the target position while maintaining an accurate robot state using close only features. When the robot is closer to the target and most of its field of view is covered by the target, it is shown that simultaneous correction needs to be used in order to minimize robot, target and map uncertainties in the absence of other landmarks.
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Ehrman, Lisa M. "An Algorithm for Automatic Target Recognition Using Passive Radar and an EKF for Estimating Aircraft Orientation." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/7510.
Full textAbstract:
Rather than emitting pulses, passive radar systems rely on illuminators of opportunity, such as TV and FM radio, to illuminate potential targets. These systems are attractive since they allow receivers to operate without emitting energy, rendering them covert. Until recently, most of the research regarding passive radar has focused on detecting and tracking targets. This dissertation focuses on extending the capabilities of passive radar systems to include automatic target recognition.
The target recognition algorithm described in this dissertation uses the radar cross section (RCS) of potential targets, collected over a short period of time, as the key information for target recognition. To make the simulated RCS as accurate as possible, the received signal model accounts for aircraft position and orientation, propagation losses, and antenna gain patterns. An extended Kalman filter (EKF) estimates the target's orientation (and uncertainty in the estimate) from velocity measurements obtained from the passive radar tracker. Coupling the aircraft orientation and state with the known antenna locations permits computation of the incident and observed azimuth and elevation angles. The Fast Illinois Solver Code (FISC) simulates the RCS of potential target classes as a function of these angles. Thus, the approximated incident and observed angles allow the appropriate RCS to be extracted from a database of FISC results. Using this process, the RCS of each aircraft in the target class is simulated as though each is executing the same maneuver as the target detected by the system. Two additional scaling processes are required to transform the RCS into a power profile (magnitude only) simulating the signal in the receiver. First, the RCS is scaled by the Advanced Refractive Effects Prediction System (AREPS) code to account for propagation losses that occur as functions of altitude and range. Then, the Numerical Electromagnetic Code (NEC2) computes the antenna gain pattern, further scaling the RCS. A Rician likelihood model compares the scaled RCS of the illuminated aircraft with those of the potential targets. To improve the robustness of the result, the algorithm jointly optimizes over feasible orientation profiles and target types via dynamic programming.
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Benemann, Eleonora Soares. "Comparação entre métodos de estimativa da idade óssea: Greulich & Pyle, Eklöf & Ringertz e atlas digital de Gilsanz & Ratib." Pontifícia Universidade Católica do Rio Grande do Sul, 2009. http://hdl.handle.net/10923/410.
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Made available in DSpace on 2011-12-27T14:14:10Z (GMT). No. of bitstreams: 2
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license.txt: 581 bytes, checksum: 44ea52f0b7567232681c6e3d72285adc (MD5)The current research aimed at evaluating the skeletal age, by means of hand and wrist radiographies, using the methods Greulich & Pyle (G&P) (1959), Eklöf & Ringertz (E&R) (1967) and the Gilsanz & Ratib digital atlas (G&R) (2005). The ages obtained from the three methods were compared, and the degree of concordance among them was determined. In order to accomplish these goals, a sample of 80 children was analyzed, 40 of the masculine gender and 40 of the feminine gender, varying from 7 to 15 years-old. All the radiographies of the individuals in the sample were digitized. Concerning the Greulich & Pyle method, an inspection evaluation of the images in the sample was carried out, comparing those images with the radiographies found at the standard atlas (“Radiographic atlas of skeletal development of the hand and wrist”), following the authors' instructions. For Eklöf & Ringertz method, the linear measures were carried out through the program Image Tool and, subsequently, the results were compared with the rates supposed by the authors. Referring to Gilsanz & Ratib method, the digitized images were compared with the images of the authors' digital atlas. Throughout the results, it was possible to realize that there was no expressive difference for the results between the methods Greulich & Pyle and Eklöf & Ringertz, not even for those between the methods Greulich & Pyle and Gilsanz & Ratib. Otherwise, when the rates obtained between the methods Eklöf & Ringertz and Gilsanz & Ratib were compared, statistically expressive differences were observed. In the genre comparison, important differences between the methods used were not noticed. Comparing the three methods concerning valuation of the bone age, Eklöf & Ringertz method presented the highest compatibility with the chronological age of the sample analyzed and Gilsanz & Ratib method presented the lowest one.
A presente pesquisa teve como objetivo avaliar a idade esquelética, por meio de radiografias de mão e punho, utilizando os métodos de Greulich & Pyle (G&P) (1959), Eklöf & Ringertz (E&R) (1967) e o atlas digital de Gilsanz & Ratib (G&R) (2005). Foram feitas comparações das idades obtidas pelos três métodos, e determinado o grau de concordância entre eles. Para isto, analisou-se uma amostra de 80 crianças, 40 do gênero masculino e 40 do gênero feminino, com idades entre 7 e 15 anos. Todas as radiografias dos indivíduos da amostra foram digitalizadas. Para o método de Greulich & Pyle foi realizada uma avaliação inspecional das imagens da amostra, comparando-as com as radiografias encontradas no atlas padrão (“Radiographic atlas of skeletal development of the hand and wrist”), conforme o preconizado pelos autores. Para o método de Eklöf & Ringertz as medidas lineares foram feitas através do programa Image Tool e, posteriormente, seus resultados foram comparados com os valores preconizados pelos autores. Para o método de Gilsanz & Ratib as imagens digitalizadas foram comparadas com as imagens contidas no atlas digital dos respectivos autores. Através dos resultados, verificou-se que não existiu diferença significativa para os escores entre os métodos Greulich & Pyle e Eklöf & Ringertz, nem entre os métodos Greulich & Pyle e Gilsanz & Ratib. Já, quando foram comparados os valores obtidos entre os métodos Eklöf & Ringertz e Gilsanz & Ratib, observaram-se diferenças estatisticamente significantes. Na comparação entre os gêneros não foram encontradas diferenças relevantes entre os métodos utilizados. Na comparação dos três métodos para a estimativa da idade óssea, o que apresentou a maior compatibilidade com a idade cronológica da amostra estudada foi o de Eklöf & Ringertz, e a menor foi para o método de Gilsanz & Ratib.
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Andersson, Tobias. "Increased Autonomy for Construction Equipment using Laser." Thesis, Linköping University, Automatic Control, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-59910.
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At working sites all around monotonic tasks are performed. If one were able
to automatize these kinds of tasks there would be a large economical profit to
collect. Volvo CE are in the process of developing an autonomous wheel loader,
to perform these types of monotonic, uniform tasks. The project is intended to
be performed mainly be thesis workers. This report is the eighth thesis in this
project. Earlier work has made the loader able to see a pile using a laser scanner.
The machine can also see and fill a hauler. The usage of the laser scanner can
only be made while the loader is standing still. The aim of this thesis work has
been to make the loader able to scan its environment while it is moving. To do
this an inertial measurement unit has been used for keeping track of the scanners
orientation during a scan. The work of this thesis has resulted in a working set-up
on the machine, and a robust framework for future work.
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Thornblad, Johan. "Drivetrain Modelling and Clutch Temperature Estimation in Heavy Duty Trucks." Thesis, Linköpings universitet, Fordonssystem, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-108117.
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An existing drivetrain model with clutch temperature dynamics has been used to simulate the behaviour of a heavy duty truck. During the implementation of the model in MATLAB/Simulink modularity and simplicity was greatly emphasized. This was done in order to facilitate the use of the model in various applications as well as making it easy to understand. The main contributions of the thesis is however the adaptation of a clutch temperature and wear observer for use in an on-line application in the gearbox management system (GMS). The process of taking the observer from an off-line simulation environment to running on-line includes taking into consideration the configuration and limitations of the GMS as well as adapting the interface of the observer. Concretely this means dealing with the limitations of the available data types in the GMS, compensating for the effect of biased measurements as well as accounting for the different dynamics of the sensor-types used in the clutch.In a simulation environment the performance of the adapted observer has been studied and its ability to compensate for heat expansion and wear in the clutch shown.En existerande drivlinemodell med temperaturdynamik i kopplingen har använts för att simulera beteendet hos en lastbil. Vid implementation av modellen i MATLAB/Simulink betonades vikten av en enkel och modulär struktur. Detta gjordes för att underlätta användning av modellen i olika applikationer samt för att göra den lätt att förstå.De huvudsakliga bidragen i uppsatsen är anpassningen av en temperatur- och slitageobeservatör på kopplingen för användning i realtid av växellådans styrenhet. För att ta observatören från simulerings- till realtidsmiljö måste styrenhetens konfiguration och begränsningar beaktas samt gränssnittet hos observatören anpassas. Konkret betyder detta att hänsyn till begränsningarna hos de olika datatyper som används i kopplingens styrenhet tagits, att den negativa inverkan som brusiga mätsignaler kan få begränsats samt att skillnader i dynamik hos de olika sensortyper som används i kopplingen kompenserats för. Med simuleringar har prestandan hos den anpassade observatören studerats samt dess förmåga att kompensera för värmeutvidgning och slitage i kopplingen visats.
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Brown, Andrew P., and Ronald A. Iltis. "DISTRIBUTED TERRESTRIAL RADIOLOCATION USING THE RLS ALGORITHM." International Foundation for Telemetering, 2002. http://hdl.handle.net/10150/605572.
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International Telemetering Conference Proceedings / October 21, 2002 / Town & Country Hotel and Conference Center, San Diego, CaliforniaThis paper presents the development of two distributed terrestrial radiolocation algorithms that use range estimates derived from DS-CDMA waveforms. The first algorithm, which is RLS-based, is derived as the solution of an approximate least-squares positioning problem. This algorithm has the advantage of reduced computational complexity, compared with the EKF-based algorithm that is presented. It is shown via simulations that both positioning algorithms perform well, with the performance of the EKF-based algorithm being superior.
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Berggren, Henrik, and Martin Melin. "UKF and EKF with time dependent measurement and model uncertainties for state estimation in heavy duty diesel engines." Thesis, Linköpings universitet, Fordonssystem, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-69229.
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The continuous challenge to decrease emissions, sensor costs and fuel consumption in diesel engines is battled in this thesis. To reach higher goals in engine efficiency and environmental sustainability the prediction of engine states is essential due to their importance in engine control and diagnosis. Model output will be improved with help from sensors, advanced mathematics and non linear Kalman filtering. The task consist of constructing non linear Kalman Filters and to adaptively weight measurements against model output to increase estimation accuracy. This thesis shows an approach of how to improve estimates by nonlinear Kalman filtering and how to achieve additional information that can be used to acquire better accuracy when a sensor fails or to replace existing sensors. The best performing Kalman filter shows a decrease of the Root Mean Square Error of 75 % in comparison to model output.
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Roussillon, Cyril. "Une solution opérationnelle de localisation pour des véhicules autonomes basée sur le SLAM." Thesis, Toulouse, INSA, 2013. http://www.theses.fr/2013ISAT0050/document.
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Les applications de la robotique mobile autonome en environnements extérieurs sont nombreuses : surveillance de site à la recherche d’anomalies, campagne d’acquisition de données,exploration, recherche de victimes sur des lieux de catastrophes, etc, et l’intérêt de la robotique pour ces applications est d’autant plus grand que les environnements peuvent être dangereux ou risqués pour l’homme. La localisation des robots est une fonction clé dans ces contextes car elle est indispensable à de nombreuses autres fonctions, particulièrement la construction de modèles d’environnement, l’exécution des trajectoires, ou la supervision des missions. Ces travaux présentent la construction d’une solution de localisation pour des robots autonomes,conçue pour être à la fois un outil générique de recherche et un outil opérationnel pour localiser nos robots lors de leurs missions de navigation autonome, capable de gérer de fortes dynamiques de mouvement. En partant d’une solution de localisation et cartographie simultanées (SLAM) basée sur l’utilisation d’une simple caméra, différentes solutions sont successivement construites en ajoutant progressivement des capteurs afin de pallier les difficultés rencontrées lors des évaluations, et ce jusqu’à obtenir un système robuste et précis combinant plusieurs caméras, une centrale inertielle et l’odométrie, et ayant en outre la possibilité d’intégrer des estimations de positions absolues quand elles peuvent être produites (par un récepteur GPS ou un algorithme exploitant une carte initiale). Une analyse profonde des capacités et limitations des différents systèmes est systématiquement effectuée, en considérant notamment l’intérêt d’estimer enligne les calibrages extrinsèques et biais des capteurs. Un accent particulier est mis sur l’exécution temps réel des algorithmes à bord du robot et sur leur robustesse : cela implique la résolution de nombreux problèmes, portant notamment sur les aspects temporels de la gestion des données. Une large évaluation sur différents jeux de données réalistes permet d’évaluer et de valider les différents développements proposés tout au long du manuscritThere are numerous applications of outdoor autonomous mobile robots : surveillance of areas to detect anomalies, data acquisition campaigns, exploration, search of victims in disaster areas, etc. Robot localization is a key function in these contexts because it is necessary fora lot of essential robotics tasks, in particular to build environment models, follow paths or supervise the execution of the missions.This work presents the development of a localization solution for autonomous robots, designedto be both a generic research tool and an effective tool to localize robots navigating with highlydynamic movements.Starting with a simultaneous localization and mapping (SLAM) solution using a single camera,several solutions are successively built by gradually adding sensors, until obtaining a robust and precise system combining several cameras, an inertial sensor and odometry, which is inaddition able to integrate absolute position measures when available (e.g. as provided byGPS or a map-based localization scheme). A in-depth analysis of the abilities and limitations of the different systems is systematically made, in particular considering the advantages of estimating online extrinsic calibration parameters and sensor biases. A particular emphasisis set on real time execution of the algorithms on board the robots and on their robustness,requiring to address various problems related to temporal aspects of data management.Thorough evaluations using different realistic datasets allows to evaluate and validate the proposed work throughout the manuscript
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Nilsson, Sanna. "Sensor Fusion for Heavy Duty Vehicle Platooning." Thesis, Linköpings universitet, Reglerteknik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-78970.
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The aim of platooning is to enable several Heavy Duty Vehicles (HDVs) to drive in a convoy and act as one unit to decrease the fuel consumption. By introducing wireless communication and tight control, the distance between the HDVs can be decreased significantly. This implies a reduction of the air drag and consequently the fuel consumption for all the HDVs in the platoon. The challenge in platooning is to keep the HDVs as close as possible to each other without endangering safety. Therefore, sensor fusion is necessary to get an accurate estimate of the relative distance and velocity, which is a pre-requisite for the controller. This master thesis aims at developing a sensor fusion framework from on-board sensor information as well as other vehicles’ sensor information communicated over a WiFi link. The most important sensors are GPS, that gives a rough position of each HDV, and radar that provides relative distance for each pair of HDV’s in the platoon. A distributed solution is developed, where an Extended Kalman Filter (EKF) estimates the state of the whole platoon. The state vector includes position, velocity and length of each HDV, which is used in a Model Predictive Control (MPC). Furthermore, a method is discussed on how to handle vehicles outside the platoon and how various road surfaces can be managed. This master thesis is a part of a project consisting of three parallel master’s theses. The other two master’s theses investigate and implement rough pre-processing of data, time synchronization and MPC associated with platooning. It was found that the three implemented systems could reduce the average fuel consumption by 11.1 %.
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Enberg, David. "Performance Evaluation of Short Time Dead Reckoning for Navigation of an Autonomous Vehicle." Thesis, Linköpings universitet, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-115881.
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Utilizing a Global Navigation Satellite System (GNSS) together with an Inertial Navigation System (INS) is today a common integration method to obtain a positioning solution for autonomous systems. Both GNSS and INS have benefits and weaknesses where the best parts from both systems can be combined with a Kalman filter. Because of this complementary nature, it is of interest to look at the robustness of the positioning solution when the Global Navigation Satellite System is temporarily not available. The aim of the thesis has been to investigate different vehicle models and to evaluate their short-time performance using a Dead Reckoning approach. The goal has been to develop a system for a Heavy Duty Vehicle (HDV) and to find out for which time interval a specific model can stay within a certain range when the GNSS is lost. A GNSS outage could for example happen when driving on a highway and passing signs, bridges and especially when driving inside tunnels. Also, for a solution to become commercially interesting, it must be cheap. Therefore, is it common to use so called Micro-Electro-Mechanical-Systems (MEMS) sensors which are of low-cost but suffer from biases, scale factors and temperature dependencies which must be compensated for. The results from the tests show that some models are able to estimate the position with good precision during short time GNSS outages whereas other models do not deliver the required accuracy. The main conclusion is that care should be taken when choosing the vehicle model so that it fits its usage area and the complexity needed to describe its motion. There are also lots of parameters to look at when investigating the best solution, where modeling of the low-cost sensors is one of them.
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Pettersson, Martin. "Extended Kalman Filter for Robust UAV Attitude Estimation." Thesis, Linköpings universitet, Reglerteknik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-119097.
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Attitude estimation of unmanned aerial vehicles is of great importance as it enables propercontrol of the vehicles. Attitude estimation is typically done by an attitude-heading refer-ence system (ahrs) which utilises different kind of sensors. In this thesis these include agyroscope providing angular rates measurements which can be integrated to describe the at-titude as well as an accelerometer and a magnetometer, both of which can be compared withknown reference vectors to determine the attitude. The sensor measurements are fused usinga gps augmented 7-state Extended Kalman filter (ekf) with a quaternion and gyroscope bi-ases as state variables. It uses differentiated gps velocity measurements to estimate externalaccelerations as reference vector to the accelerometer, which significantly raises robustnessof the solution. The filter is implemented in MatlabTM and in c on an ARM microprocessor.It is compared with an explicit complementary filter solution and is evaluated with flightsusing a fixed-wing uav with satisfactory results.
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Stenström, Jonathan. "Simultaneous Trajectory Optimization and Target Estimation Using RSS Measurements to Land a UAV." Thesis, Linköpings universitet, Reglerteknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-131117.
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The use of autonomous UAV’s is a progressively expanding industry. This thesisfocuses on the landing procedure with the main goal to be independent of visualaids. That means that the landing site can be hidden from the air, the landingcan be done in bad weather conditions and in the dark. In this thesis the use ofradio signals is investigated as an alternative to the visual sensor based systems.A localization system is needed to perform the landing without knowing wherethe landing site is. In this thesis an Extended Kalman Filter (EKF) is derived andused for the localization, based on the received signal strength from a radio beaconat the landing site. There are two main goals that are included in the landing,to land as accurate and as fast as possible. To combine these two goals a simultaneoustrajectory optimization and target estimation problem is set up that can bepartially solved while flying. The optimal solution to this problem produces thepath that the UAV will travel to get the best target localization while still reachingthe target. It is shown that trying to move directly towards the estimated landingsite is not the best strategy. Instead, the optimal trajectory is a spiral that jointlyoptimizes the information from the sensors and minimizes the arrival time.
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Kroeger, Kenneth Edward. "Design and Evaluation of a Fixed-Pitch Multirotor UAV with a Nonlinear Control Strategy." Thesis, Virginia Tech, 2013. http://hdl.handle.net/10919/23109.
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The use and practical applications of small UAV systems has continually grown in the past several years in both the public and private sectors. These UAV systems are used for not only defensive purposes, but for commercial applications such as exterior bridge and home inspections, wildlife/wildfire management and observation, conservation exercises, law-enforcement, radio-repeating operations, and a wide variety of other uses that may not warrant the use, expense, space constraints, or risk of a manned aircraft. This thesis focuses on the design of a fixed pitch multirotor UAV system for use in furthering research projects and facilitating payload data collection from a flying platform without the expense or risk of testing with available larger UAV systems.The design of a multirotor UAV system with a flight control scheme, communication architecture and hardware, electrical architecture and hardware, and mechanical design is presented. An Extended Kalman Filter (EKF) strategy is implemented aboard a developed Inertial Measurement Unit (IMU) to estimate vehicle state. Experiments then validated the estimates from the EKF through a comparative approach between the developed unit and a commercial unit. A nonlinear flight control system is implemented based on an Integral-Backstepping control strategy. The flight control strategy was then fully simulated and exhaustively tested under a variety of external disturbances and initial conditions from a fully dynamic modeled environment. Parameters about the vehicle were experimentally determined to increase the accuracy of the model which would increase the chances of successful flight operations.
Flight demonstrations were conducted to evaluate the abilities and performance of the control system, along with testing the interface abilities and reliability between a universal ground control station (UGCS) and the aircraft. Lastly, the model was revisited with the input data from the flight control experiment and the output captured was evaluated against the output of the model system to evaluate effectiveness, reliability, and accuracy of the model. The results of the comparison showed that the computer simulation was accurate in predicting attitude and altitude of the vehicle to that of the realized system.
Master of Science
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Johansson, Jenny, and Mikaela Waller. "Control Oriented Modeling of the Dynamics in a Catalytic Converter." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-4154.
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Avgasmängden som bensindrivna fordon tillåts släppa ut minskas hela tiden. Ett sätt att möta framtida krav, är att förbättra katalysatorns effektivitet. För att göra detta kan luft-bränsle-förhållandet regleras med avseende på syrelagringen i katalysatorn, istället för som idag, reglera mot stökiometriskt blandningsförhållande. Eftersom syrelagringen inte går att mäta med en givare behövs en modell som beskriver katalysatorns dynamiska egenskaper. Tre sådana modeller har undersökts, utvärderats och jämförts.
Två av modellerna har implementerats i Matlab/Simulink och anpassats till mätningar från en experimentuppställning. För att kunna observera syrelagringen online valdes slutligen en av modellerna ut, och implementerades i ett Extended Kalman filter.
Ytterligare arbete behöver läggas ner på den mest lovande modellen, och detsamma gäller för Kalmanfiltret, men på sikt förväntas resultaten kunna bli bra.
The legal amount of emissions that vehicles with spark ignited engines are allowed to produce are steadily reduced over time. To meet future emission requirements it is desirable to make the catalytic converter work in a more efficient way. One way to do this is to control the air-fuel-ratio according to the oxygen storage level in the converter, instead of, as is done today, always trying to keep it close to stoichiometric. The oxygen storage level cannot be measured by a sensor. Hence, a model describing the dynamic behaviors of the converter is needed to observe this level. Three such models have been examined, validated, and compared.
Two of these models have been implemented in Matlab/Simulink and adapted to measurements from an experimental setup. Finally, one of the models was chosen to be incorporated in an extended Kalman filter (EKF), in order to make it possible to observe the oxygen storage level online.
The model that shows best potential needs further work, and the EKF is working with flaws, but overall the results are promising.
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Larsson, Jesper. "The Effect of Simulink Block Kalman Filters in a CubeSat ADCS." Thesis, KTH, Rymdteknik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-285762.
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The purpose of this paper was to implement Kalman filtering in the form of pre-existing Simulink blocks into a CubeSat attitude determination and control system simulation and to evaluate their performance. In recent versions of Simulink, the block library has been expanded, providing a new level of abstraction for simulation engineers. The capabilities of such library filter blocks have previously not been explored for space applications and could offer a faster and more simplified filter integration process. Three types of filter implementations have been realized, being classic Kalman filter, extended Kalman filter and unscented Kalman filter. These have been applied to the outputs of the coarse Sun sensor and Earth horizon sensor, as well as to the simulation attitude estimate. State propagation functions have been defined in the form of constant and linear approximations in addition to state propagation following the same structure as the simulation reference truth. Filter efficiency was evaluated using control error, pointing knowledge, pointing accuracy and variance as performance measures. Furthermore, interventions were introduced in the form of sensor data loss and solar panel deployment. The Kalman filter blocks were successfully integrated into the simulation. Performance measures revealed that constant state transition functions provided the best performance in most cases, exceptions being the extended Kalman filter and unscented Kalman filter of the attitude estimate application. Here, the true state propagation instead outperformed the other filters. Signal data loss showed that the true state propagation was the only one that could accurately predict the attitude state in a scenario when sensors fail to provide data. Solar panel deployment could not be utilized to evaluate the filter performance as the filter implementation did not support prediction of a dynamic attitude state. Results suggest that the pre-existing Simulink filter blocks can provide an easier alternative to defining filters from scratch. However, great care needs to be taken when tuning block parameters and constructing state transition functions to assure proper behavior.Syftet med arbetet har varit att implementera Kalmanfilter i formen av fördefinierade Simulink-block i en simulering av ett system för attitydbestämning och styrning för en CubeSat, och utvärdera prestandan. I nyare versioner av Simulink har blockbiblioteken utökats, vilket har introducerat nya nivåer av abstraktion för simuleringsingenjörer. Möjligheterna hos filterblock i sådana bibliotek har ännu inte utforskats för rymdtekniska tillämpningar, och skulle kunna leda till snabbare och enklare integrering av filter. Tre typer av filterimplementationer har genomförts: klassiska Kalmanfilter, utökat Kalmanfilter och oparfymerat Kalmanfilter. Dessa har applicerats till utdata från solsensor och jordhorisontsensor, samt till simuleringens uppskattade attityd. Funktioner för tillståndspropagering har definierats i formen av konstanta och linjära approximationer tillsammans med den verkliga tillståndspropageringen, som har samma struktur som simuleringens sanna referensvärde. Effektiviteten hos filtren har utvärderats genom kontrollfel, riktningskunskap, riktningsnoggrannhet och varians som prestandamått. Vidare har interventioner introducerats i form av förlust av sensordata och utfällning av solpaneler. Kalmanfilterblocken integrerades med framgång i simuleringen. Prestandamåtten visade att de konstanta funktionerna för tillståndspropagering gav bäst prestanda i de flesta fallen, förutom i fallet av utökat Kalmanfilter och oparfymerat Kalmanfilter i appliceringen på den uppskattade attityden. I det sistnämnda fallet var det den verkliga tillståndspropageringen som presterade bättre än de andra filtren. Förlust av signaldata visade att den verkliga tillståndspropageringen är den enda som med säkerhet kan förutsäga utvecklingen av attityden i ett läge där sensorerna inte längre levererar data. Utfällningen av solpanelerna kunde inte utnyttjas för att utvärdera prestandan hos filtren, då implementeringen av filtren inte kan förutsäga utvecklingen av ett dynamiskt attitydtillstånd. Resultaten antyder att fördefinierade Simulink-filter kan erbjuda ett enklare alternativ till att definiera filter helt från början. Dock så krävs noga omsorg vid inställning av blockparametrar och vid konstruktion av funktioner för tillståndspropagering för att säkerställa korrekt beteende
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Shareef, Haider. "Gyrobiasskattning för små autonoma flygplan." Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8387.
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En patenterad metod [11] ligger till grund för utförandet av det här examensarbetet där målet var att beskriva metoden i reglertekniska termer. En lösning för att kompensera för gyrodrifter var också önskvärd. En olinjär modell som beskriver flygplanets dynamik har tagits fram och simulerats. Modellen har linjäriserats kring en trajektoria och diskretiserats för att undersöka observerbarheten för systemet. Huruvida ett system är observerbart eller inte avgör om det är möjligt eller inte att applicera ett extended kalmanfilter (EKF) på för att skatta systemets tillstånd. Kalmanfiltret används för att man inte har tillgång till mätningar på systemets alla tillstånd och måste därför skatta dem. Olika tester har gjorts för att undersöka kalmanfiltrets prestanda. Trots att kalmanfiltret är ett optimalt filter kan man inte garantera tillfredsställande resultat eftersom antalet tillgängliga mätbara tillstånd påverkar hur bra skattningar man får.
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Lowe, Matthew. "Extended and Unscented Kalman Smoothing for Re-linearization of Nonlinear Problems with Applications." Digital WPI, 2015. https://digitalcommons.wpi.edu/etd-dissertations/457.
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The Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Ensemble Kalman Filter (EnKF) are commonly implemented practical solutions for solving nonlinear state space estimation problems; all based on the linear state space estimator, the Kalman Filter. Often, the UKF and EnKF are cited as a superior methods to the EKF with respect to error-based performance criteria. The UKF in turn has the advantage over the EnKF of smaller computational complexity. In practice however the UKF often fails to live up to this expectation, with performance which does not surpass the EKF and estimates which are not as robust as the EnKF. This work explores the geometry of alternative sigma point sets, which form the basis of the UKF, contributing several new sets along with novel methods used to generate them. In particular, completely novel systems of sigma points that preserve higher order statistical moments are found and evaluated. Additionally a new method for scaling and problem specific tuning of sigma point sets is introduced as well as a discussion of why this is necessary, and a new way of thinking about UKF systems in relation to the other two Kalman Filter methods. An Iterated UKF method is also introduced, similar to the smoothing iterates developed previously for the EKF. The performance of all of these methods is demonstrated using problem exemplars with the improvement of the contributed methods highlighted.
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Nylén, Rebecka, and Katherine Rajala. "Development of an ICP-based Global Localization System." Thesis, Linköpings universitet, Reglerteknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178858.
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The most common way to track the position of a vehicle is by using the Global Navigation Satellite System (GNSS). Unfortunately, there are many scenarios where GNSS is inaccessible or provides low precision, and it can therefore be vulnerable to only rely on GNSS. This master's thesis is done in collaboration with the Swedish Defence Research Agency (FOI), who is looking for a solution to this problem. Therefore, this master's thesis develops a system that globally localizes a vehicle in a map, without GNSS. The approach is to combine odometry and the scan registration algorithm iterative closest point (ICP), in an extended Kalman filter (EKF), to provide global position estimates. The ICP algorithm aligns two different sets of data points, referred to as point clouds. In this thesis, one set consists of light detection and ranging (LIDAR) data points collected from a sensor mounted on a vehicle, and the other consists of LIDAR data points collected from an aircraft which forms an elevation map of the area. In the ideal case, the algorithm finds the position on the elevation map where the vehicle collected the data points. For the EKF to function, the uncertainty of ICP must be estimated. Different methods are investigated, which are; unscented transform based covariance, covariance with Hessian, and covariance with correspondences. The result shows that all the methods are too optimistic when estimating the uncertainty. The reason is that none of the methods take all sources of error into account, and it is therefore difficult to correctly capture the uncertainty of ICP. The unscented transform based covariance is the least optimistic, and covariance with correspondences is the most. A second problem investigated in this thesis is how odometry and ICP with an elevation map as reference can be combined to provide a global position estimate. As mentioned, the chosen approach is to implement an EKF which weights the different data sources based on their covariance, to one single estimate. The developed global localization system is evaluated in a real time experiment, where the data is recorded using equipment from FOI. The goal of the experiment is to localize a vehicle while it is driving in different environments, including urban, field and forest environments. The result shows that the performance of the system is viable, and it manages to provide localization within a few meters from ground truth. However, since the ICP covariance estimates are not fully accurate, the performance of the EKF is decreased as it cannot weight the different estimates properly. The ICP algorithm used in the system has a lot of flaws. The worst is that it easily converges to incorrect solutions, in other words that it estimates the wrong position of the vehicle. How this risk can be decreased is also investigated in this thesis. A method that decreases this risk drastically, and makes the viable performance of the system possible, is developed. The approach of the method is to exclude incorrect positions by removing a large amount of points from the point clouds, and keeping the most informative. By only utilizing the most informative data points in the point cloud, global positions with high accuracy are achieved.
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Salhi, Imane. "Intelligent embadded camera for robust object tracking on mobile platform." Thesis, Paris Est, 2021. http://www.theses.fr/2021PESC2001.
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Les systèmes de navigation visuelle-inertielle (VINS) est une thématique d'actualité, difficile à traiter, notamment lorsqu’il s’agit de respecter les contraintes des systèmes embarqués. Les questions relatives à la miniaturisation, la portabilité et la communication (en ce qui concerne, par exemple, l’IoT) des systèmes électroniques s’inscrivent dans des problématiques actuelles en matière d'avancée technologique. Pour répondre de manière efficace à ces problématiques, il est nécessaire d’envisager des traitements complexes et des implémentations sur des supports contraignants en termes d’intégration et de consommation d’énergie, tels que les micro véhicules aériens (MAV), les drones (UAV), les lunettes intelligentes et les caméras intelligentes.Au cours de cette dernière décennie, différents algorithmes de suivi et de VINS ont été développés. Ils sont suffisamment précis mais ils nécessitent des ressources calculatoires conséquentes, dans l'objectif de pouvoir réaliser un suivi performant, compte tenu des différentes formes d'utilisation possibles. Or, les systèmes embarqués imposent de fortes contraintes d'intégration, ce qui réduit leurs ressources, particulièrement en termes de capacité calculatoire. Par conséquent, ce type de système nécessite de recourir à des algorithmes efficaces avec moins de charge et de complexité calculatoire. En effet, tout l’enjeu de cette thèse réside dans cette problématique. L'objectif de ce travail est d’apporter une solution de suivi qui permettrait d'assurer un fonctionnement robuste dans différents environnements de navigation, adaptée à l'implémentation sur une architecture embarquée. Le système proposé dans cette thèse est nommé « système de suivi inertiel-visuel adaptatif à l'environnement de navigation »Initially, the aim of this study is to analyze, compare and retain the most relevant tracking methods likely to respect the constraints of embedded systems, such as Micro Aerial Vehicles (MAVs), Unmanned Aerial Vehicles (UAVs), intelligent glasses, textit{etc.} in order to find a new robust embedded tracking system. A typical VINS consists of a monocular camera that provides visual data (frames), and a low-cost Inertial Measurement Unit (IMU), a Micro-Electro-Mechanical System (MEMS) that measures inertial data. This combination is very successful in the application field of system navigation thanks to the advantages that these sensors provide, mainly in terms of accuracy, cost and rapid reactivity. Over the last decade, various sufficiently accurate tracking algorithms and VINS have been developed, however, they require greater computational resources. In contrast, embedded systems are characterized by their high integration constraints and limited resources. Thus, a solution to embedded architecture must be based on efficient algorithms that provide less computational load.As part of this work, various tracking algorithms identified in the literature are discussed, focusing on their accuracy, robustness, and computational complexity. In parallel to this algorithmic survey, numerous recent embedded computation architectures, especially those dedicated to visual and/or visual-inertial tracking, are also presented. In this work, we propose a robust visual-inertial tracking method, called: "Context Adaptive Visual Inertial SLAM". This approach is adaptive to different system navigation context and well fitted for embedded systems such as MAVs. It focuses on the analysis of the impact of navigation condition on the tracking process in an embedded system. It also provides an execution control module able to switch between the most relevant tracking approaches by monitoring its internal state and external inputs variables. The main objective is to ensure tracking continuity and robustness despite difficult conditions
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Rosén, Jakob. "A Framework for Nonlinear Filtering in MATLAB." Thesis, Linköping University, Department of Electrical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-5190.
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The object of this thesis is to provide a MATLAB framework for nonlinear filtering in general, and particle filtering in particular. This is done by using the object-oriented programming paradigm, resulting in truly expandable code. Three types of discrete and nonlinear state-space models are supported by default, as well as three filter algorithms: the Extended Kalman Filter and the SIS and SIR particle filters. Symbolic expressions are differentiated automatically, which allows for comfortable EKF filtering. A graphical user interface is also provided to make the process of filtering even more convenient. By implementing a specified interface, programming new classes for use within the framework is easy and guidelines for this are presented.
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Villeneuve, Hubert. "Height Estimation of a Blimp Unmanned Aerial Vehicle Using Inertial Measurement Unit and Infrared Camera." Thesis, Université d'Ottawa / University of Ottawa, 2017. http://hdl.handle.net/10393/36635.
Full textAbstract:
Increasing demands in areas such as security, surveillance, search and rescue, and communication, has promoted the research and development of unmanned aerial vehicles (UAVs) as such technologies can replace manned flights in dangerous or unfavorable conditions. Lighter-than-air UAVs such as blimps can carry higher payloads and can stay longer in the air compared to typical heavier-than-air UAVs such as aeroplanes or quadrotors. One purpose of this thesis is to develop a sensor suite basis for estimating the position and orientation of a blimp UAV in development with respect to a reference point for safer landing procedures using minimal on-board sensors. While the existing low-cost sensor package, including inertial measurement unit (IMU) and Global Navigation System (GPS) module, could be sufficient to estimate the pose of the blimp to a certain extent, the GPS module is not as precise in the short term, especially for altitude. The proposed system combines GPS and inertial data with information from a grounded infrared (IR) camera. Image frames
are processed to identify three IR LEDs located on the UAV and each LED coordinate is estimated using a Perspective-n-Point (PnP) algorithm. Then the results from the PnP algorithm are fused with the GPS, accelerometer and gyroscope measurements using an Extended Kalman Filter (EKF) to get a more accurate estimate of the position and the orientation. Tests were conducted on a simulated blimp using the experimental avionics.
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Sinivaara, Kristian. "Simultaneous Localisation and Mapping using Autonomous Target Detection and Recognition." Thesis, Linköpings universitet, Reglerteknik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-110410.
Full textAbstract:
Simultaneous localisation and mapping (SLAM) is an often used positioning approach in GPS denied indoor environments. This thesis presents a novel method of combining SLAM with autonomous/aided target detection and recognition (ATD/R), which is beneficial for both methods. The method uses physical objects that are recognisable by ATR as unambiguous features in SLAM, while SLAM provides the ATR with better position estimates. The intended application is to improve the positioning of a first responder moving through an indoor environment, where the map offers localisation and simultaneously helps locate people, furniture and potentially dangerous objects like gas cannisters. The developed algorithm, dubbed ATR-SLAM, uses existing methods from different fields such as EKF-SLAM and ATR based on rectangle estimation. Landmarks in the form of 3D point features based on NARF are used in conjunction with identified objects and 3D object models are used to replace landmarks when the same information is used. This leads to a more compact map representation with fewer landmarks, which partly compensates for the introduced cost of the ATR. Experiments performed using point clouds generated from a time-of-flight laser scanner show that ATR-SLAM produces more consistent maps and more robust loop closures than EKF-SLAM using only NARF landmarks.