Kamerabasierte Verfahren zur Steuerung autonomer mobiler Roboter wurden in den letzten Jahren immer populärer. In dieser Arbeit wird der Einsatz eines Stereokamerasystems und eines Mono-Kamera-SLAM Verfahrens hinsichtlich der Unterstützung der Navigation eines autonomen Luftschiffes untersucht. Mit Hilfe von Sensordaten aus IMU, GPS und Kamera wird eine Positionsschätzung über eine Sensorfusion mit Hilfe des Extended und des Unscented Kalman Filters durchgeführt.

Thesis (doctoral)--Universität St. Gallen, 2001.

Reproduction de : Thèse de doctorat : Génie électrique : Toulouse, INPT : 2008. Reproduction de : Thèse de doctorat : Génie électrique : Université de Tsinghua, Pékin : 2008. / Thèse soutenue en co-tutelle. Texte en chinois, résumé étendu en français. Titre provenant de l'écran-titre. Bibliogr. 38 réf.

Thesis (Ph. D.)--University of Washington, 2001. / Vita. Includes bibliographical references (leaves 131-139).

Thesis (Ph. D.)--University of Texas at Austin, 2003. / Vita. Includes bibliographical references. Available also from UMI Company.

Thesis (M.S. in Mechanical Engineering)--Naval Postgraduate School, June 2004. / Thesis advisor(s): Isaac I. Kaminer. Includes bibliographical references (p. 69). Also available online.

Thesis (M.S. in Electrical Engineering)--Naval Postgraduate School, June 2003. / Thesis advisor(s): Hal A. Titus. Includes bibliographical references (p. 89-90). Also available online.

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.

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A radiolocation/navigation system is considered in which mobile nodes use ultra-wideband (UWB) radios to obtain inter-node ranges via round-trip travel time (RTT). Each node is also assumed to contain an inertial measurement unit (IMU) which generates 2D position estimates subject to Gaussian drift and additive noise errors. The key problem in such a system is obtaining 2 or 3-D position estimates from the nonlinear UWB range measurements and fusing the resulting UWB and IMU estimates. The system presented uses a Steepest Descent Random Start (SDRS) algorithm to solve the nonlinear positioning problem. It is shown that SDRS is a stable algorithim under a realistic communications reciprocity assumption. The SDRS estimates are then treated as measurements by the navigation Kalman filter. The navigation filter also processes separate IMU-derived position estimates to update node position/velocity. Simulation results for an urban corridor are given showing < 6 m. rms position errors.