Localization and mapping are two of the most central tasks when it comes toautonomous robots. It has often been performed using expensive, accurate sensorsbut the fast development of consumer electronics has made similar sensorsavailable at a more affordable price. In this master thesis a TurtleBot\texttrademark\, robot and a MicrosoftKinect\texttrademark\, camera are used to perform Simultaneous Localization AndMapping, SLAM. The thesis presents modifications to an already existing opensource SLAM algorithm. The original algorithm, based on visual odometry, isextended so that it can also make use of measurements from wheel odometry and asingle axis gyro. Measurements are fused using an Extended Kalman Filter,EKF, operating in a multirate fashion. Both the SLAM algorithm and the EKF areimplemented in C++ using the framework Robot Operating System, ROS. The implementation is evaluated on two different data sets. One set isrecorded in an ordinary office room which constitutes an environment with manylandmarks. The other set is recorded in a conference room where one of the wallsis flat and white. This gives a partially sparse featured environment. The result by providing additional sensor information is a more robust algorithm.Periods without credible visual information does not make the algorithm lose itstrack and the algorithm can thus be used in a larger variety of environmentsincluding such where the possibility to extract landmarks is low. The resultalso shows that the visual odometry can cancel out drift introduced bywheel odometry and gyro sensors.

In several projects within ABB there is a need of state and parameter estimation for nonlinear dynamic systems. One example is a project investigating optimisation of gas turbine operation. In a gas turbine there are several parameters and states which are not measured, but are crucial for the performance. Such parameters are polytropic efficiencies in compressor and turbine stages, cooling mass flows, friction coefficients and temperatures. Different methods are being tested to solve this problem of system identification or parameter estimation. This thesis describes the implementation of such a method and compares it with previously implemented identification methods. The comparison is carried out in the context of parameter estimation in gas turbine models, a dynamic load model used in power systems as well as models of other dynamic systems. Both simulated and real plant measurements are used in the study.

To make driving easier and safer, modern vehicles are equipped with driver support systems. Some of these systems, for example navigation or curvature warning systems, need the global position of the vehicle. To determine this position, the Global Positioning System (GPS) or a Dead Reckoning (DR) system can be used. However, these systems have often certain drawbacks. For example, DR systems suffer from error growth with time and GPS signal masking can occur. By integrating the DR position and the GPS position, the complementary characteristics of these two systems can be used advantageously. In this thesis, low cost in-vehicle sensors (gyroscope and speedometer) are used to perform DR and the GPS receiver used has a low update frequency. The two systems are integrated with an extended Kalman filter in order to estimate a position. The evaluation of the implemented positioning algorithmshows that the system is able to give an estimated position in the horizontal plane with a relatively high update frequency and with the accuracy of the GPS receiver used. Furthermore, it is shown that the system can handle GPS signal masking for a period of time. In order to increase the performance of a positioning system, map matching can be added. The idea with map matching is to compare the estimated trajectory of a vehicle with roads stored in a map data base, and the best match is chosen as the position of the vehicle. In this thesis, a simple off-line map matching algorithm is implemented and added to the positioning system. The evaluation shows that the algorithm is able to distinguish roads with different direction of travel from each other and handle off-road driving.

Today’s diesel engines are complex with systems like VGT and EGR to be able to fulfil the stricter emission legislations and the demands on the fuel consumption. Controlling a system like this demands a sophisticated control system. Furthermore, the authorities demand on self diagnosis requires an equal sophisticated diagnosis system. These systems require good knowledge about the signals present in the system and how they affect each other. One way to achieve this is to have a good model of the system and based on this calculate an observer. The observer is then used to estimate signals used for control and diagnosis. Advantages with an observer instead of using just sensors are that the sensor signals often are noisy and need to be filtered before they can be used. This causes time delay which further complicates the control and diagnosis systems. Other advantages are that sensors are expensive and that some engine quantities are hard to measure. In this Master’s thesis a model of a Scania diesel engine is developed and an observer is calculated. Due to the non-linearities in the model the observer is based on a constant gain extended Kalman filter.

A lane departure warning system relying exclusively on a camera has several shortcomings and tends to be sensitive to, e.g., bad weather and abrupt manoeuvres. To handle these situations, the system proposed in this thesis uses a dynamic model of the vehicle and integration of relative motion sensors to estimate the vehicle’s position on the road. The relative motion is measured using vision, inertial, and vehicle sensors. All these sensors types are affected by errors such as offset, drift and quantization. However the different sensors are sensitive to different types of errors, e.g., the camera system is rather poor at detecting rapid lateral movements, a type of situation which an inertial sensor practically never fails to detect. These kinds of complementary properties make sensor fusion interesting. The approach of this Master’s thesis is to use an already existing lane departure warning system as vision sensor in combination with an inertial measurement unit to produce a system that is robust and can achieve good warnings if an unintentional lane departure is about to occur. For the combination of sensor data, different sensor fusion models have been proposed and evaluated on experimental data. The models are based on a nonlinear model that is linearized so that a Kalman filter can be applied. Experiments show that the proposed solutions succeed at handling situations where a system relying solely on a camera would have problems. The results from the testing show that the original lane departure warning system, which is a single camera system, is outperformed by the suggested system.

In wireless positioning system, in addition to channel error, the geometric re- lationship between sensor nodes and the target may also affect the positioning accuracy. The effect is called geometric dilution of precision (GDOP). GDOP is determined as ratio factor between location error and measurement error. A higher GDOP value indicates a larger location error in location estimation. Accordingly, the location performance will be poor. The GDOP can therefore be used as an in- dex of the positioning performance. In this thesis, approaches of tracking a moving target with extended Kalman filter (EKF) in a time-difference-of-arrival (TDOA) wireless positioning system are discussed. While the target changes its position with time, the geometric layout between sensor nodes and the target will become differ- ent. To maintain the good layout, the positioning system with mobile sensor nodes is considered. Therefore, the geometric layout can be possibly improved and GDOP effect can be reduced by the mobility of mobile sensor nodes. In order to find the positions that mobile sensor nodes should move to, a time-varying function based on the GDOP distribution is defined for finding the best solutions. Since the simu- lated annealing is capable of escaping local minima and finding the global minimum in an objective function, the simulated annealing algorithm is used in finding the best solutions in the defined function. Thus the best solutions can be determined as the destinations of mobile sensor nodes. When relocating mobile sensor nodes from their current positions to the destinations, they may pass through or stay in high GDOP regions before arriving at the destinations. To avoid the problem, we establish an objective function for path planning of mobile sensor nodes in order to minimize the overall positioning accuracy. Simulation results show that the mobile sensor nodes will accordingly change their positions while the target is moving. All the sensor nodes will maintain a surrounding region to localize the target and the GDOP effect can be effectively reduced.

The thesis is to explore wireless location tracking algorithms based on geometric dilution of precision (GDOP) in the mobile environment. The GDOP can be used as an indication of positioning accuracy, affected by the geometric relationship between the target and sensing units. The smaller the GDOP is, the better positioning accuracy. By using the information of sensing units and time difference of arrival (TDOA) positioning method, we use extended Kalman filter as an estimator to track and predict the state of a moving target. From previous research, the lowest GDOP value, located at the center of a regular polygon, represents the best positioning accuracy in 2-D scenario with numerous sensing units. It is important to find the best locations for the sensing units. Simulated annealing algorithm was used in previous studies. However, it only finds a location at a time, and consumes computation load and time. Due to the above-mentioned reasons, we propose a location tracking system, which consists of a base traiver station and numerous mobile sensing units. By using the information of a base transceiver station and the predicted position of target, we can obtain the best locations for all the mobile sensing units with the calculation of rotation matrix. The locations can also be used as beacons for relocating mobile sensing units. It may take many cycles to move mobile sensing units to the best locations. We have to perform path planning for mobile sensing units. Due to the location change of the moving target, the routes need adjustment accordingly. If the predicted stay of a mobile sensing unit is inside the obstacle, we adjust the route of the mobile sensing unit to make it stay out of the obstacle. Therefore, we also propose a path planning scheme for mobile sensing units to avoid obstacles. Through simulations, the proposed method decreases the tracking time effectively, and find the best locations precisely. When mobile sensing units move toward the best locations, they successfully avoid obstacles and move toward the position with the minimum GDOP. Through the course, good positioning accuracy can be maintained.

This dissertation explores both the estimation of various parameters from a multiple antenna GPS receiver, which is used as an attitude sensor, and attitude determination using GPS-like Pseudolite signals. To use a multiple antenna GPS receiver as an attitude sensor, parameters such as baselines, integer ambiguities, line biases, and attitude, should be resolved beforehand. Also, due to a cycle slip problem a subsystem to correct this problem should be implemented. All of these tasks are called a self survey. A new algorithm to estimate these parameters from a GPS receiver is developed usingnonlinear batch filteringmethods.For convergence issues, both the nolinear least squares (NLS) and Levenberg-Marquardt (LM) methods are applied in the estimation.Acomparison ofthe NLSand LMmethods shows that the convergence of the LM method for the large initial errors is more robust than that of the NLS. In the proximity of the International Space Station (ISS), Pseudolite signals replace the GPSsignals since almostallsignals are blocked.Since the Pseudolite signals have spherical wavefronts, a new observation model should be applied. A nonlinear predictive filter, an extended Kalman filter (EKF), and an unscented filter (UF) are developed and compared using Pseudolite signals. A nonlinear predictive filter can provide a deterministic solution; however, it cannot be used for the moving case. Instead, the EKF or the UF can be used with the angular rate measurements. A comparison of EKF and UF shows that the convergence of the UF for the large initial errors is more robust than that of the EKF. Also, an alternative global navigation constellation is presented by using the Flower Constellation (FC) scheme. A comparison of FC global navigation constellation and other GPS constellations, U.S. GPS, Galileo, and GLONASS, shows that position and attitude errors of the FC constellation are smaller that those of the others.

The conventional method of thermal modeling of space payloads is expensive and cumbersome. Radiation plays an important part in the thermal modeling of space payloads because of the presence of vacuum and deep space viewing. This induces strong nonlinearities into the thermal modeling process. There is a need for extensive correlation between the model and test data. This thesis presents Online Parameter Estimation as an approach to automate the thermal modeling process. The extended Kalman fillter (EKF) is the most widely used parameter estimation algorithm for nonlinear models. The unscented Kalman filter (UKF) is a new and more accurate technique for parameter estimation. These parameter estimation techniques have been evaluated with respect to data from ground tests conducted on an experimental space payload.

<p>In today’s heavy industry there are applications demanding high power supply in certain periods of a working cycle. A typical case might be startup of heavy machinery or just keeping a certain point in a distribution network at a certain energy level. To deal with this different techniques might be used, one way is to introduce a battery as an energy reserve in the system. One battery studied at ABB for this purpose is the so called High Temperature Sodium Nickel Chloride battery and a model of this battery has been developed at ABB. When operating a battery of the mentioned type in an application it is important to keep track of the energy stored in the battery. Earlier tests has shown that this is difficult in a noisy environment.</p><p>This master thesis investigates if a Kalman filter may be used to estimate the energy stored in the battery. The investigation is performed in steps, starting with a simplified model of the battery and then expanding to a more complete model. Evaluation of the methods and algorithms used is made by simulations and based on the assumption that there is a good model available. The model is special in such a way that it has a varying number of states despite that the number of outputs remains the same.</p><p>Some comparisons with actual measurements are also made and an analysis of the parameters in the model along with an introduction to the system identification problem is discussed, assuming that the structure of the model is correct.</p> / <p>I dagens tunga industri finns applikationer som kräver höga effektuttag under vissa perioder av en arbetscykel. Ett typiskt fall kan vara uppstart av tunga maskiner eller att hålla en given spänningsnivå i en belastningspunkt i ett distributionsnät. För att hantera detta finns olika metoder, en möjlighet är att använda ett batteri som en energireserv. Ett högtemperaturbatteri har studerats på ABB för detta ändamål och en model av detta batteri har tagits fram. När ett sådant batteri används är det viktigt att kontinuerligt veta hur mycket energi som finns till förfogande i batteriet. Tidigare tester har visat att detta är svårt i en brusig miljö.</p><p>I detta examensarbete kommer det undersökas om ett Kalman filter kan användas för att skatta energin i detta batteri. Undersökningen sker i steg och startar med en förenklad modell som sedan utvecklas till en mer komplett modell. Utvärdering av de metoder och algoritmer som används sker via simuleringar och baseras på antagandet att modellen är komplett och riktig. Denna modell är speciell på det sätt att den har ett variabelt antal tillstånd trots att antalet utsignaler är konstant.</p><p>Viss jämförelse med de mätningar som finns tillgängliga görs och en inledande analys av de ingående modellparametrarna presenteras. Även en introduktion till det omfattande systemidentifieringsproblemet diskuteras, med antagandet att modellens struktur är korrekt.</p>