With an increasing traffic intensity the demands on vehicular safety is higher than ever before. Active safety systems that have been developed recent years are a response to that. In this master thesis Sensor Fusion is used to combine information from a laser scanner and a microwave radar in order to get more information about the surroundings in front of a vehicle. The Extended Kalman Filter method has been used to fuse the information from the sensors. The process model consists partly of a Constant Turn model to describe the motion of the ego vehicle as well as a tracked object. These individual motions are then put together in a framework for spatial relationships to describe the relationship between them. Two measurement models have been used to describe the two sensors. They have been derived from a general sensor model. This filter approach has been used to estimate the position and orientation of an object relative the ego vehicle. Also velocity, yaw rate and the width of the object have been estimated. The filter has been implemented and simulated in Matlab. The data that has been recorded and used in this work is coming from a scenario where the ego vehicle is following an object in a quite straight line. Where the ego vehicle is a truck and the object is a bus. One important conclusion from this work is that the filter is sensitive to the number of laser beams that hits the object of interest. No qualitative validation has been made though.

This thesis aims the problem of underwater vehicle positioning. The vehicle usedwas a Saab Seaeye Falcon which was equipped with a Doppler Velocity Log(DVL)manufactured by RD Instruments and an inertial measurement unit (IMU) fromXsense. During the work several different Extended Kalman Filter (EKF) havebeen tested both with a hydrodynamic model of the vehicle and a model withconstant acceleration and constant angular velocity. The filters were tested withdata from test runs in lake Vättern. The EKF with constant acceleration andconstant angular velocity appeared to be the better one. The misalignment of thesensors were also tried to be estimated but with poor result.

Autonomous vehicles are an emerging trend in robotics, seen in a vast range of applications and environments. Consequently, Volvo Construction Equipment endeavour to apply the concept of autonomous vehicles onto one of their main products. In the company’s Autonomous Machine project an autonomous wheel loader is being developed. As an ob jective given by the company; a demonstration proving the possibility of conducting a fully autonomous load and haul cycle should be performed. Conducting such cycle requires the vehicle to be able to localise itself in its task space and navigate accordingly. In this Master’s Thesis, methods of solving those requirements are proposed and evaluated on a real wheel loader. The approach taken regarding localisation, is to apply sensor fusion, by extended Kalman filtering, to the available sensors mounted on the vehicle, including; odometric sensors, a Global Positioning System receiver and an Inertial Measurement Unit. Navigational control is provided through an interface developed, allowing high level software to command the vehicle by specifying drive paths. A path following controller is implemented and evaluated. The main objective was successfully accomplished by integrating the developed localisation and navigational system with the existing system prior this thesis. A discussion of how to continue the development concludes the report; the addition of a continuous vision feedback is proposed as the next logical advancement.

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.

In this report, mainly three different problems are considered. The first problem considered is how to filter position data of vehicles. To do so the vehicles have to be tracked. This is done with Kalman filters. The second problem considered is how to control a camera to keep a vehicle in the center of the image, under three different conditions. This is mainly solved with a Kalman filter. The last problem considered is how to use the color of the vehicles to make classification of them more robust. Some suggestions on how this might be done are given. However, no really good method to do this has been found. / Den här rapporten behandlar huvudsakligen tre olika problem. Det första problemet är hur man ska filtrera fordons positions data. För att göra detta måste fordonen följas. Detta är gjort med ett Kalmanfilter. Det andra problemet var att styra en kamera så att ett givet fordon ligger mitt i bild, tre olika förhallånde har betraktats. Detta löstes huvudsakligen med ett Kalmanfilter. Det sista problemet var hur man ska använda fordonens färg så att man får säkrare klassificering av dem. Några förslag på hur detta kan göras ges, men ingen riktigt bra metod har hittats.

In this master thesis, a real-time control system that stabilizes the rotational rates of a tri-copter, has been studied. The tricopter is a rotorcraft with three rotors. The tricopter has been modelled and identified, using system identification algorithms. The model has been used in a Kalman filter to estimate the state of the system and for design ofa model based controller. The control approach used in this thesis is a model predictive controller, which is a multi-variable controller that uses a quadratic optimization problem to compute the optimal con-trol signal. The problem is solved subject to a linear model of the system and the physicallimitations of the system. Two different types of algorithms that solves the MPC problem have been studied. These are explicit MPC and the fast gradient method. Explicit MPC is a pre-computed solution to the problem, while the fast gradient method is an online solution. The algorithms have been simulated with the Kalman filter and were implemented on themicrocontroller of the tricopter.

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

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

This master thesis studies distributed navigation which isa function implemented in a future network based combat information system to improve the accuracy in navigation for combat vehicles in a mechanized battalion, above all in the event of loss of GPS. In the event of loss of the GPS the vehicles obtain dead reckoning performance through the backup system that consists of an odometer and a magnetic compass. Dead reckoning means a drift in the position that makes the accuracy in the navigation worse. The distributed navigation function uses position and navigation data with measurements between the vehicles to estimate the errors and uncertainties in positions, which are used to improve the accuracy in position for the vehicles. To investigate and demonstrate distributed navigation, a simulation environment has been produced in Matlab. The environment is general so different navigation systems can be used and studied and also dynamical so a further development is possible. The simulation environment has been used to investigate and evaluate an implementation of distributed navigation. The implementation has been made using a central filter where fusion takes place of all navigation data and measurements. This filter has been realized with help of Kalman filter theory, in which all vehicles are put together in a state space model. Simulations have been performed for different scenarios and the result of these show that the drift in position is avoided.

The use of unmanned systems is becoming widespread in commercial and military sectors. The ability of these systems to take on dull, dirty, and dangerous tasks which were formerly done by humans is encouraging their rapid adoption. In particular, a subset of these undesirable tasks are uniquely suited for small unmanned aerial vehicles such as quadrotor helicopters. Examples of such tasks include surveillance, mapping, and search and rescue. Many of these potential tasks require quadrotors to be deployed in environments where a degree of position estimation is required and traditional GPS-based positioning technologies are not applicable. Likewise, since unmanned systems in these environments are often intended to serve the purpose of scouts or first--responders, no maps or reference beacons will be available. Additionally, there is no guarantee of clear features within the environment which an onboard sensor suite (typically made up of a monocular camera and inertial sensors) will be able to track to maintain an estimate of vehicle position. Up to 90% of the features detected in the environment may produce motion estimates which are inconsistent with the true vehicle motion. Thus, new methods are needed to compensate for these environmental deficiencies and measurement inconsistencies. In this work, a RANSAC-based outlier rejection technique is combined with an Extended Kalman Filter (EKF) to generate estimates of vehicle position in a 2--D plane. A low complexity feature selection technique is used in place of more modern techniques in order to further reduce processor load. The overall algorithm was faster than the traditional approach by a factor of 4. Outlier rejection allows the abundance of low quality, poorly tracked image features to be filtered appropriately, while the EKF allows a motion model of the quadrotor to be incorporated into the position estimate. The algorithm is tested in real-time on a quadrotor vehicle in an indoor environment with no clear features and found to be able to successfully estimate position of the vehicle to within 40 cm, superior to those produced when no outlier rejection technique was used. It is also found that the choice of simple feature selection approaches is valid, as complex feature selection approaches which may take over 10 times as long to run still result in outliers being present. When the algorithm is used for vehicle control, periodic synchronization to ground truth data was required due to nearly 1 second of latency present in the closed--loop system. However, the system as a whole is a valid proof of concept for the use of low quality images for quadrotor position control. The overall results from the work suggest that it is possible for unmanned systems to use visual data to estimate state even in operational environments which are poorly suited for visual estimation techniques. The filter algorithm described in this work can be seen as a useful tool for expanding the operational capabilities of small aerial vehicles.