Do we want to share our lives and bodies with robots? A 2000 people survey

For roughly two decades a new generation of robots, robotic prostheses and implantable devices is about to arise accompanied by great optimism that they will widely pervade our daily life in a near future. This paper presents the results from a survey on the question if people want to share their life and body with robots. The survey, carried out in connection with the Robotics exhibition at the Swiss National Exhibition Expo.02, counts over 2000 participants. The questionnaire covers issues on robotics in general, service and personal robots, robotic prostheses and artificial organs. While the results testify a positive attitude towards potential robotic co-workers, flat-mates or body part, they include a number of surprising answers. We find correlations in the data, discuss interpretations, speculate about the answers and cultural influences and finally conclude: Whom are we building robots for and what should they be like? To whom are we selling robots and how should we market them

Sensordatenfusion zur Robusten und präzisen EKF Lokalisierung von mobilen Robotern

Diese Arbeit beschreibt einen Ansatz zur Lokalisierung von Mobilrobotern mittels der Kombination eines Laserscanners mit monokularem Video. Das Verfahren ist merkmalsbasiert und benutzt ein erweitertes Kalman filter (EKF) zur Datenfusion und Positionsschätzung. Die Umgebungsmerkmale sind Liniensegmente für den Laserscanner und vertikale Kanten für die Kamera. Physikalisch gut basierte Unsicherheitsmodelle beider Sensoren werden eingesetzt und bei Sensorkalibration und Merkmalsextraktion in Betracht gezogen. Dies liefert die geschätzten ersten zwei Momente der Merkmalsvektoren. Die Experimente, die auf einem vollständig autonomen Roboter durchgeführt wurden, zielten auf zwei Fragestellungen ab: In welchem Mass kann das Hinzufügen video-basierter Umgebungsinformation die Navigation hinsichtlich Robustheit und Präzision verbessern? Die dazu ausgeführten Experimente zeigen, dass gerade in schwierigen Lokalisierungsszenarien wie lange Korridore, die Bildinformation einen unerlässlichen Beitrag liefert und in der Lage ist, die Positionsschätzung im allgemeinen und besonders in der Orientierung zu verbessern

Improving Robustness and Precision in Mobile Robot Localization by Using Laser Range Finding and Monocular Vision

This paper discusses mobile robot localization by means of geometric features from a laser range finder and a CCD camera. The features are line segments from the laser scanner and vertical edges from the camera. Emphasis is put on sensor models with a strong physical basis. For both sensors, uncertainties in the calibration and measurement process are adequately modeled and propagated through the feature extractors. This yields observations with their first order covariance estimates which are passed to an extended Kalman filter for fusion and position estimation. Experiments on a real platform show that opposed to the use of the laser range finder only, the multisensor setup allows the uncertainty to stay bounded in difficult localization situations like long corridors and contributes to an important reduction of uncertainty, particularly in the orientation. The experiments further demonstrate the applicability of such a multisensor localization system in real-time on a fully autonomous robot