<p>Knowledge about the current road slope can improve several applications in a heavy-duty vehicle such as predictive cruise control and automated gearbox control. In this thesis the possibility of estimating the road slope based on signals from a vehicles air suspension system has been studied. More specifically, the measurement consists of a pressure signal measuring the axle load, and a vertical distance sensor.</p><p>A variety of suspension systems can be mounted on a Scania truck. During this thesis, two discrete-time models based on two different rear axle air suspension systems have been proposed. The models use the effect of alternating axle load during a change in the road slope and the estimates are computed using an extended Kalman filter.</p><p>The first model is based on a rear axle suspension known as the 2-bellow system. This type of suspension is strongly affected by the driveshaft torque, which results in a behaviour where the rear end is pushed upwards and thus decreasing the rear axle load during uphill driving. A model was developed in order to compensate for this behaviour. Unfortunately, the estimates showed less promising results and all attempts to determine the error was unsuccessful.</p><p>The latter model is based on the 4-bellow system. This suspension system is not affected by the driveshaft torque and a less complex model could be derived. The experimental results indicated that road slope estimation was possible and with a fairly accurate result. However, more work is needed since the estimate is affected by road surface irregularities and since the algorithm requires knowledge about the vehicles mass and the location of the centre of gravity.</p><p>All the presented results have been estimated based on real data from a test track at Scania Technical Centre in Södertälje.</p>

<p>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.</p><p>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.</p><p>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.</p>

<p>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.</p>

<p>A face tracker is exactly what it sounds like. It tracks a face in a video sequence. Depending on the complexity of the tracker, it could track the face as a rigid object or as a complete deformable face model with face expressions.</p><p>This report is based on the work of a real time feature based face tracker. Feature based means that you track certain features in the face, like points with special characteristics. It might be a mouth or eye corner, but theoretically it could be any point. For this tracker, the latter is of interest. Its task is to extract global parameters, i.e. rotation and translation, as well as dynamic facial parameters (expressions) for each frame. It tracks feature points using motion between frames and a textured face model (Candide). It then uses an extended Kalman filter to estimate the parameters from the tracked feature points.</p>

<p>Scania CV AB is a large manufacturer of heavy duty trucks that, with an increasingly stricter emission legislation, have a rising demand for an effective On Board Diagnosis (OBD) system. One idea for improving the OBD system is to employ a model for the construction of an observer based diagnosis system. The proposal in this report is, because of a nonlinear model, to use a nonlinear filtering method for improving the needed state estimates. Two nonlinear filters are tested, the Particle Filter (PF) and the Extended Kalman Filter (EKF). The primary objective is to evaluate the use of the PF for Fault Detection and Isolation (FDI), and to compare the result against the use of the EKF.</p><p>With the information provided by the PF and the EKF, two residual based diagnosis systems and two likelihood based diagnosis systems are created. The results with the PF and the EKF are evaluated for both types of systems using real measurement data. It is shown that the four systems give approximately equal results for FDI with the exception that using the PF is more computational demanding than using the EKF. There are however some indications that the PF, due to the nonlinearities, could offer more if enough CPU time is available.</p>

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.

For the environment of target tracking with highly non-linear models and non-Gaussian noise, the tracking performance of the particle filter is better than extended Kalman filter; in addition, the design of particle filter is simpler, so it is quite suitable for the realistic environment. However, particle filter depends on the probability model of the noise. If the knowledge of the noise is incorrect, the tracking performance of the particle filter will degrade severely. To tackle the problem, cost function-based particle filters have been studied. Though suffering from minor degradation on the performance, the cost function-based particle filters do not need probability assumptions of the noises. The application of cost function-based particle filters will be more robust in any realistic environment. Cost function-based particle filters will enable maneuvering multiple target tracking to be suitable for any environment because it does not depend on the noise model. The difficulty lies in the link between the estimator and data association. The likelihood function are generally obtained from the algorithm of the data association; while cost functions are used in the cost function-based particle filter for moving the particles and update the corresponding weights without probability assumptions on the noises. The thesis is focused on the combination of data association and cost function-based particle filter, in order to make the algorithm of multiple target tracking more robust in noisy environments.

A perfect duality exists between the problem of space-based orbit determination from line-of-sight measurements and the problem of designing an interplanetary autonomous navigation system. Mathematically, these two problems are equivalent. Any method solving the first problem can be used to solve the second one and, vice versa. While the first problem estimates the observed unknown object orbit using the known observer orbit, the second problem does exactly the opposite (e.g. the spacecraft observes a known visible planet). However, in an interplanetary navigation problem, in addition to the measurement noise, the following "perturbations" must be considered: 1) light-time effect due to the finite speed of light and large distances between the observer and planets, and 2) light aberration including special relativistic effect. These two effects require corrections of the initial orbit estimation problems. Because of the duality problem of space-based orbit determination, several new techniques of angles-only Initial Orbit Determination (IOD) are here developed which are capable of using multiple observations and provide higher orbit estimation accuracy and also they are not suffering from some of the limitations associated with the classical and some newly developed methods of initial orbit determination. Using multiple observations make these techniques suitable for the coplanar orbit determination problems which are the case for the spacecraft navigation using visible planets as the solar system planets are all almost coplanar. Four new IOD techniques were developed and Laplace method was modified. For the autonomous navigation purpose, Extended Kalman Filter (EKF) is employed. The output of the IOD algorithm is then used as the initial condition to extended Kalman filter. The two "perturbations" caused by light-time effect and stellar aberration including special relativistic effect also need to be taken into consideration and corrections should be implemented into the extended Kalman filter scheme for the autonomous spacecraft navigation problem.

No description available.

Localization and mapping are two of the most central tasks when it comes to autonomous robots. It has often been performed using expensive, accurate sensors but the fast development of consumer electronics has made similar sensors available at a more affordable price. In this master thesis a TurtleBot, robot and a Microsoft Kinect, camera are used to perform Simultaneous Localization And Mapping, SLAM. The thesis presents modifications to an already existing open source SLAM algorithm. The original algorithm, based on visual odometry, is extended 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 are implemented in C++ using the framework Robot Operating System, ROS. The implementation is evaluated on two different data sets. One set is recorded in an ordinary office room which constitutes an environment with many landmarks. The other set is recorded in a conference room where one of the walls is 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 its track and the algorithm can thus be used in a larger variety of environments including such where the possibility to extract landmarks is low. The result also shows that the visual odometry can cancel out drift introduced by wheel odometry and gyro sensors.