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.

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.

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.

International Telemetering Conference Proceedings / October 21, 2002 / Town & Country Hotel and Conference Center, San Diego, California / This 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.

In the last decades, the quadrotor has been used in many areas, and deigning an effective flight control algorithm for the quadrotor has attracted great interests in both control and robotics communities. This thesis focuses on the flight control of the quadrotor by using different methods: The extend Kalman filter (EKF) based linear quadratic regulator (LQR) method and learning-based model predictive control (LBMPC) method. Chapter 4 investigates the flight control of a quadrotor subject to the model uncertainties and external disturbances. We propose a LQR based tracking algorithm. However, the designed LQR controller is hard to be implemented because of the existing noises in the measured states. A modified EKF is then designed for the online estimation of the position, velocity and motor dynamics by using the measured outputs. From the experimental testing results, it is shown that the proposed EKF-based LQR control method solves the tracking problem of the quadrotor with less tracking errors than only using the LQR method. In Chapter 5, the tracking control problem of the quadrotor subject to external disturbances and physical constraints is studied. A model predictive control (MPC) based algorithm is proposed. To reduce the computational load, a modified prior barrier interior-point method is used to solve the quadratic programming (QP) problem. Nevertheless, the achievable flight performance by using the standard MPC algorithm is affected by external disturbances. A LBMPC algorithm is proposed for the disturbance rejection. From the simulation results, it is shown that using the proposed LBMPC algorithm have less tracking errors than applying the standard MPC algorithm. / Graduate

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.

<p>At working sites all around monotonic tasks are performed. If one were able</p><p>to automatize these kinds of tasks there would be a large economical profit to</p><p>collect. Volvo CE are in the process of developing an autonomous wheel loader,</p><p>to perform these types of monotonic, uniform tasks. The project is intended to</p><p>be performed mainly be thesis workers. This report is the eighth thesis in this</p><p>project. Earlier work has made the loader able to see a pile using a laser scanner.</p><p>The machine can also see and fill a hauler. The usage of the laser scanner can</p><p>only be made while the loader is standing still. The aim of this thesis work has</p><p>been to make the loader able to scan its environment while it is moving. To do</p><p>this an inertial measurement unit has been used for keeping track of the scanners</p><p>orientation during a scan. The work of this thesis has resulted in a working set-up</p><p>on the machine, and a robust framework for future work.</p><p> </p>

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.

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 %.

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.