Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1981. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / by John A. Cartelli. / M.S.

Thesis. 1977. M.S.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / John C. Doyle. / M.S.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1981. / Microfiche copy available in Archives and Barker / Bibliography: leaves 108-109. / by William Neil McCasland. / M.S.

Thesis. 1978. Ph.D.--Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Vita. / Bibliography: leaves 305-312. / by William Henry Ledsham, Jr. / Ph.D.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Ocean Engineering, 1980. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Bibliography: leaves 289-293. / by Wei-Yuan Hwang. / Ph.D.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1979. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND AERONAUTICS. / Bibliography: leaf 72. / by Yuji Hanawa. / M.S.

Thesis. 1977. Ocean E.--Massachusetts Institute of Technology. Dept. of Ocean Engineering. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERING. / Includes bibliographical references. / Ocean E.

Thesis. 1977. M.S.--Massachusetts Institute of Technology. Dept. of Aeronautics and Astronautics. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND AERONAUTICS. / Bibliography : leaves 127-133. / M.S.

Tsang, Chi Chiu. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2008. / Includes bibliographical references (leaves 66-69). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iii / Statement of Originality --- p.v / Table of Contents --- p.vii / List of Figures --- p.x / Nomenclature --- p.xii / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Motivation --- p.1 / Chapter 1.2 --- Objectives --- p.3 / Chapter 1.3 --- Contributions --- p.3 / Chapter 1.4 --- Thesis Organization --- p.4 / Chapter 2 --- A Ubiquitous Digital Writing System --- p.5 / Chapter 2.1 --- Introduction --- p.5 / Chapter 2.2 --- MEMS Motion Sensing Technology --- p.6 / Chapter 2.2.1 --- Micro-Electro-Mechanical Systems (MEMS) --- p.6 / Chapter 2.2.2 --- Principle of a MEMS Accelerometer --- p.6 / Chapter 2.2.3 --- Principle of a MEMS Gyroscope --- p.7 / Chapter 2.3 --- Architecture of Ubiquitous Digital Writing System --- p.8 / Chapter 2.3.1 --- Micro Inertial Measurement Unit (μlMU) --- p.8 / Chapter 2.3.2 --- Data Transmission Module --- p.10 / Chapter 2.3.3 --- User Interface Software --- p.10 / Chapter 2.4 --- Summary --- p.12 / Chapter 3 --- Calibration of μ-Inertial Measurement Unit --- p.13 / Chapter 3.1 --- Introduction --- p.13 / Chapter 3.2 --- Sources of Error --- p.13 / Chapter 3.2.1 --- Deterministic Errors --- p.13 / Chapter 3.2.2 --- Stochastic Error --- p.14 / Chapter 3.3 --- Calibration of Accelerometers --- p.14 / Chapter 3.4 --- Coordinate Transformation with Gravity Compensation --- p.15 / Chapter 3.4.1 --- Coordinate Transformation --- p.16 / Chapter 3.4.2 --- Attitude Determination --- p.18 / Chapter 3.4.3 --- Gravity Compensation --- p.19 / Chapter 3.5 --- Summary --- p.20 / Chapter 4 --- Zero Velocity Compensation --- p.21 / Chapter 4.1 --- Introduction --- p.21 / Chapter 4.2 --- Algorithm Description --- p.21 / Chapter 4.2.1 --- Stroke Segmentation --- p.22 / Chapter 4.2.2 --- Zero Velocity Compensation (ZVC) --- p.22 / Chapter 4.3 --- Experimental Results and Discussion --- p.23 / Chapter 4.4 --- Summary --- p.24 / Chapter 5 --- Kalman Filtering --- p.28 / Chapter 5.1 --- Introduction --- p.28 / Chapter 5.2 --- Summary of Kalman filtering algorithm --- p.28 / Chapter 5.2.1 --- System Model --- p.28 / Chapter 5.2.2 --- Initialization --- p.29 / Chapter 5.2.3 --- Time Update --- p.32 / Chapter 5.2.4 --- Measurement Update --- p.33 / Chapter 5.2.5 --- Stroke Segmentation --- p.34 / Chapter 5.3 --- Summary --- p.34 / Chapter 6 --- Error Compensation from Position Feedback --- p.35 / Chapter 6.1 --- Introduction --- p.35 / Chapter 6.2 --- Global Positioning System (GPS) --- p.35 / Chapter 6.3 --- Zero z-axis Kalman Filtering --- p.36 / Chapter 6.3.1 --- Algorithm Implementation --- p.36 / Chapter 6.3.2 --- Experimental Results and Discussion --- p.40 / Chapter 6.4 --- Combined Electromagnetic Resonance (EMR) Position Detection Board and μlMU --- p.43 / Chapter 6.4.1 --- EMR Position Detection System --- p.43 / Chapter 6.4.2 --- A Combined Scheme --- p.44 / Chapter 6.4.3 --- Algorithm Implementation --- p.46 / Chapter 6.4.4 --- Synchronization --- p.50 / Chapter 6.4.5 --- Experimental Results and Discussion --- p.50 / Chapter 6.5 --- Summary --- p.54 / Chapter 7 --- Conclusion --- p.55 / Chapter 7.1 --- Future Work --- p.56 / Chapter 7.1.1 --- Improvement in the μlMU --- p.56 / Chapter 7.1.2 --- Combined Camera Optical Tracking and μlMU --- p.57 / Chapter 7.2 --- Concluding Remarks --- p.58 / Chapter A --- Derivation of Kalman Filtering Algorithm --- p.59 / Chapter A.1 --- Introduction --- p.59 / Chapter A.2 --- Derivation of a Priori State Estimation Equation --- p.60 / Chapter A.3 --- Derivation of a Posteriori State Estimation Equation --- p.60 / Chapter A.4 --- Derivation of a Priori Error Covariance Matrix --- p.61 / Chapter A.5 --- Derivation of the Optimal Kalman Gain --- p.62 / Chapter A.6 --- Derivation of a Posteriori Error Covariance Matrix --- p.63 / Chapter B --- Derivation of Process Noise Covariance Matrix --- p.64 / Bibliography --- p.66 / Publications --- p.69

Robot localization is one of the most important subjects in the Robotics science. It is an interesting and complicated topic. There are many algorithms to solve the problem of localization. Each localization system has its own set of features, and based on them, a solution will be chosen. In my thesis, I want to present a solution to find the best estimate for a robot position in certain space for which a map is available. The thesis started with an elementary introduction to the probability and the Gaussian theories. Simple and advanced practical examples are presented to illustrate each concept related to localization. Extended Kalman Filter is chosen to be the main algorithm to find the best estimate of the robot position. It was presented through two chapters with many examples. All these examples were simulated in Matlab in this thesis in order to give the readers and future students a clear and complete introduction to Kalman Filter. Fortunately, I applied this algorithm on a robot that I have built its base from scratch. MCECS-Bot was a project started in Winter 2012 and it was assigned to me from my adviser, Dr. Marek Perkowski. This robot consists of the base with four Mecanum wheels, the waist based on four linear actuators, an arm, neck and head. The base is equipped with many sensors, which are bumper switches, encoders, sonars, LRF and Kinect. Additional devices can provide extra information as backup sensors, which are a tablet and a camera. The ultimate goal of this thesis is to have the MCECS-Bot as an open source system accessed by many future classes, capstone projects and graduate thesis students for education purposes. A well-known MRPT software system was used to present the results of the Extended Kalman Filter (EKF). These results are simply the robot positions estimated by EKF. They are demonstrated on the base floor of the FAB building of PSU. In parallel, simulated results to all different solutions derived in this thesis are presented using Matlab. A future students will have a ready platform and a good start to continue developing this system.