Tasse, Stefan2014-04-022014-04-022014-04-02http://hdl.handle.net/2003/3300710.17877/DE290R-13703Gathering information, especially about the immediately surrounding world, is a central aspect of any smart device, whether it is a robot, a partially autonomous vehicle, or a mobile handheld device. The consequential use of electrical sensors always implies the need to filter the imperfect sensor data output in order to gain reliable information. While the challenge of perception and cognition in machines is not a new one, new technology constantly opens up new possibilities and challenges. This is stressed further by the advent of cheap sensor technology and the possibility to use a multitude of small sensors, with the simultaneous constraint of limited resources on mobile, battery-powered computing devices. In this work, stochastic methods are used to filter sensor data, which is gathered by mobile devices, to model the devices' location and eventually also relevant parts of their dynamic environment. This is done with a focus on online algorithms and computation on these mobile devices themselves, which implies limited available processing power and the necessity for computational efficiency. This dissertation's purpose is to impart a better understanding about the conception and design of stochastic filtering solutions, to propose localization algorithms beyond the current state of the art, and to show the use of simultaneous localization and mapping algorithms in the context of cooperatively estimating the surrounding world of a team of robots in a fast changing, dynamic environment. To achieve these goals, the concepts are depicted in multiple application scenarios, design choices and their implications systematically cover all aspects of sensing and estimation, and the proposed systems are evaluated in real-world experiments on humanoid robots and other mobile devices.enLocalizationTrackingStochastic modelingRoboticsKalmanParticle filter004doctoral thesis