Satellite control with saturating inputs

I would like to express my sincere gratitude to those who helped me to complete this endeavor: A mes directeurs de thèse, Dr. Sophie Tarbouriech et Dr. Christophe Prieur, pour son support et sa patience aux moments difficiles de cette thèse. Merci pour m’aider dans les moments de doute et pour être toujours à l’écoute. J’espère Christophe que tu aies profité des tapas à Barcelone. Sophie, merci pour ton dernier mot au jury de thèse, je vais essayer de garder toujours mon côté séducteur. A mes responsables industriels, Dr. Christelle Pittet et Dr. Catherine Charbonnel, qui ont été toujours très attentifs aux évolutions de la thèse et qui ont agit comme deux vrais directeurs de thèse et pas comme de superviseurs. Je veux remercier Christelle par son exigence. Parfois c’était dur mais le remarques les plus dures sont celles qui te font avancer le plus. A Catherine juste lui dire que chaque fois qu’on a travaillé ensemble cela a été un vrai plaisir. ThankyoutoallthePhDcommitteeforthetimeinvestedinthereadingandcorrection of my dissertation. A special thanks to Prof. Caroline Berard as she was the person wh

Advisor: Dr. habil. Fritjhof Kruggel, MPI for Human Cognitive and Brain Sciences, Leipzig

zur Erlangung des akademischen Grades DOCTOR rerum naturalium (Dr. rer. nat.) im Fachgebiet Informatik vorgelegt von Dipl. math. Gert Wollny geboren am 31.08.1968 in Leipzi

Kartierung von mobilen Robotern in Innenräumen Submitted to the

This thesis addresses the Simultaneous Localization and Mapping (SLAM) problem, a key prob-lem for any truly autonomous mobile robot. The task for the robot is to build a map of its environment and simultaneously determine its own position in the map while moving. The problem is examined from an estimation-theoretic perspective. The focus is on the core estimation algorithm which provides an estimate for the map and robot pose from two sensor inputs: The first sensor is odometry, i.e. the observation of the robot’s movement from the revo-lution of its wheels. The second is the observation of environment features, so called landmarks. The optimal solution based on maximum likelihood or least square estimation needs excessive computation time, i.e. O((n + p) 3) for n landmarks and p robot poses. Popular approaches like Extended Kalman Filter (EKF) are more efficient but still need O(n 2) computation time and suffer from linearization errors. The first contribution of this thesis is an analysis of SLAM, in particular under the aspect of the inherent uncertainty structure of a map estimate. The key result can be phrased as “Certainty of relations despite uncertainty of positions”. The discussion further analyzes the linearizatio

Date of doctorate:

I would like to express my gratitude to my mentors and colleagues without whom this work wouldn’t have been possible. First and foremost, I would like to thank Professor Heinrich Niemann, the head of the Chair for Pattern Recognition at the University of Erlangen, for teaching me the basics of the field and waking my interest in it, but also for giving me the opportunity to earn a doctorate at his chair. I owe special thanks to Dr. Elmar Nöth, head of the Speech Processing Group at the Chair for Pattern Recognition, whose sometimes critical and yet unfailingly fair opinion guided my progress throughout these years. It is Elmar from whom I have the strong conviction that science is not only about creativity and innovation but is also a great deal of fun. The role of Elmar’s benevolent participation in my career is really hard to underestimate. Among people who contributed the most to this work, I would like to particularly distinguish Dr. Allen L. Gorin. It was my honor and privilege to work in his team at the AT&T Labs. While being leader of the HMIHY project and later as Director of the Knowledge Discovery Department, Al’s professional expertise in the scientific matters and his genuine support of a friend in everyday situations remained a steady and irreplaceable source of confidence for me that I will always appreciate. For countless and very informative discussions that gradually shaped this dissertation I would also like to pay tribute to my former colleagues from the AT&T Labs and the University o