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Performance and Implementation Aspects of Nonlinear Filtering
 DEPARTMENT OF ELECTRICAL ENGINEERING, LINKÖPING UNIVERSITY
, 2008
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Estimation and Detection with Applications to Navigation
"... Papers B and E are published with permission from IFAC c ○ 2008. Paper C is published with permission from IEEE c ○ 2008. Paper D is published with permission from EURASIP c ○ 2007. The ability to navigate in an unknown environment is an enabler for truly autonomous systems. Such a system must be aw ..."
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Papers B and E are published with permission from IFAC c ○ 2008. Paper C is published with permission from IEEE c ○ 2008. Paper D is published with permission from EURASIP c ○ 2007. The ability to navigate in an unknown environment is an enabler for truly autonomous systems. Such a system must be aware of its relative position to the surroundings using sensor measurements. It is instrumental that these measurements are monitored for disturbances and faults. Having correct measurements, the challenging problem for a robot is to estimate its own position and simultaneously build a map of the environment. This problem is referred to as the Simultaneous Localization and Mapping (SLAM) problem. This thesis studies several topics related to SLAM, onboard sensor processing, exploration and disturbance detection. The particle filter (PF) solution to the SLAM problem is commonly referred to as FastSLAM and has been used extensively for ground robot applications. Having more complex vehicle models using for example flying robots extends the state dimension of
On the Design of Low Order Hinfinity Controllers
, 2011
"... when the system has one state. Any point in the interior corresponds to a controller with one state, i.e., a socalled full order controller. A point on the curved boundary corresponds to a controller with zero states, i.e., a socalled static controller or reduced order controller. ..."
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when the system has one state. Any point in the interior corresponds to a controller with one state, i.e., a socalled full order controller. A point on the curved boundary corresponds to a controller with zero states, i.e., a socalled static controller or reduced order controller.
Experimental comparison of some classical iterative learning control algorithms
 IEEE Transactions on Robotics and Automation
, 2002
"... Abstract—This letter gives an overview of classical iterative learning control algorithms. The presented algorithms are also evaluated on a commercial industrial robot from ABB. The presentation covers implicit to explicit modelbased algorithms. The result from the evaluation of the algorithms is t ..."
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Abstract—This letter gives an overview of classical iterative learning control algorithms. The presented algorithms are also evaluated on a commercial industrial robot from ABB. The presentation covers implicit to explicit modelbased algorithms. The result from the evaluation of the algorithms is that performance can be achieved by having more system knowledge. Index Terms—Design, experiment, industrial robot, iterative learning control. I.
Sensor Fusion for Automotive Applications
, 2011
"... Mapping stationary objects and tracking moving targets are essential for many autonomous functions in vehicles. In order to compute the map and track estimates, sensor measurements from radar, laser and camera are used together with the standard proprioceptive sensors present in a car. By fusing inf ..."
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Mapping stationary objects and tracking moving targets are essential for many autonomous functions in vehicles. In order to compute the map and track estimates, sensor measurements from radar, laser and camera are used together with the standard proprioceptive sensors present in a car. By fusing information from different types of sensors, the accuracy and robustness of the estimates can be increased. Different types of maps are discussed and compared in the thesis. In particular, road maps make use of the fact that roads are highly structured, which allows relatively simple and powerful models to be employed. It is shown how the information of the lane markings, obtained by a front looking camera, can be fused with inertial measurement of the vehicle motion and radar measurements of vehicles ahead to compute a more accurate and robust road geometry estimate. Further, it is shown how radar measurements of stationary targets can be used to estimate the road edges, modeled as polynomials and tracked as extended targets. Recent advances in the field of multiple target tracking lead to the use of finite set
Extended target tracking using PHD filters
"... Two persons were measured at waist height as they walked around in front of the laser range sensor. The data is plotted in three dimensions, with time along the vertical axis. Tracking results for this data set are presented in ..."
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Two persons were measured at waist height as they walked around in front of the laser range sensor. The data is plotted in three dimensions, with time along the vertical axis. Tracking results for this data set are presented in
Sensor fusion and calibration of inertial sensors, vision, ultrawideband and GPS,” Linköping Studies in Science and
 Linköping University, The Institute of Technology
, 2011
"... The usage of inertial sensors has traditionally been confined primarily to the aviation and marine industry due to their associated cost and bulkiness. During the last decade, however, inertial sensors have undergone a rather dramatic reduction in both size and cost with the introduction of microma ..."
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The usage of inertial sensors has traditionally been confined primarily to the aviation and marine industry due to their associated cost and bulkiness. During the last decade, however, inertial sensors have undergone a rather dramatic reduction in both size and cost with the introduction of micromachined electromechanical system (mems) technology. As a result of this trend, inertial sensors have become commonplace for many applications and can even be found in many consumer products, for instance smart phones, cameras and game consoles. Due to the drift inherent in inertial technology, inertial sensors are typically used in combination with aiding sensors to stabilize and improve the estimates. The need for aiding sensors becomes even more apparent due to the reduced accuracy of mems inertial sensors. This thesis discusses two problems related to using inertial sensors in combination with aiding sensors. The first is the problem of sensor fusion: how to combine the information obtained from the different sensors and obtain a good estimate of position and orientation. The second problem, a prerequisite for sensor fusion, is that of calibration: the sensors themselves have to be calibrated and provide measurement in known units. Furthermore, whenever multiple sensors are combined additional calibration issues arise, since the measurements are seldom acquired in the same physical location and expressed in a common coordinate frame. Sensor fusion and calibration are discussed for the combination of inertial sensors with cameras, ultrawideband (uwb) or global positioning system
Linear Models of Nonlinear Systems
"... Linear timeinvariant approximations of nonlinear systems are used in many applications and can be obtained in several ways. For example, using system identification and the predictionerror method, it is always possible to estimate a linear model without considering the fact that the input and outp ..."
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Linear timeinvariant approximations of nonlinear systems are used in many applications and can be obtained in several ways. For example, using system identification and the predictionerror method, it is always possible to estimate a linear model without considering the fact that the input and output measurements in many cases come from a nonlinear system. One of the main objectives of this thesis is to explain some properties of such approximate models. More specifically, linear timeinvariant models that are optimal approximations in the sense that they minimize a meansquare error criterion are considered. Linear models, both with and without a noise description, are studied. Some interesting, but in applications usually undesirable, properties of such optimal models are pointed out. It is shown that the optimal linear model can be very sensitive to small nonlinearities. Hence, the linear approximation of an almost linear system can be useless for some applications, such as robust control design. Furthermore, it is shown that standard validation methods, designed for identification of linear systems, cannot always be used to validate an optimal
Structural Reformulations in System Identification
 Thesis
"... estimation problem, where the orange curve represents the predictionerror formulation and the green curve the cost function after a reformulation using the difference algebraic techniques proposed in Chapter 8. ..."
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estimation problem, where the orange curve represents the predictionerror formulation and the green curve the cost function after a reformulation using the difference algebraic techniques proposed in Chapter 8.