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2008 2 Acknowledgements I would like to express my thanks to Prof. Roland Siegwart, the head of the Autonomous System Laboratory (ASL), without whom I would never have visited and have become a member of the ASL. He deserves my deepest grat­ itude for adopting me into the group, for providing excellent research and social facilities. Furthermore, to give me(More)
In this paper, we present a stereovision algorithm for real-time 6DoF ego-motion estimation, which integrates image intensity information and 3D stereo data in the well-known Iterative Closest Point (ICP) scheme. The proposed method addresses a basic problem of standard ICP, i.e. its inability to perform the segmentation of data points and to deal with(More)
—Mobile robots are increasingly being used in high-risk rough terrain situations, such as planetary exploration and military applications. Current control and localization algorithms are not well suited to rough terrain, since they generally do not consider the physical characteristics of the vehicle and its environment. Little attention has been devoted to(More)
The development of intelligent surveillance systems is an active research area. In this context, mobile and multi-functional robots are generally adopted as means to reduce the environment structuring and the number of devices needed to cover a given area. Nevertheless, the number of different sensors mounted on the robot, and the number of complex tasks(More)
Passive RFID provides an inexpensive and effective support to basic mobile robot navigation tasks. Nonetheless, problems related to interference and reflections of the signal, and missing tag range and bearing information are open. In this paper, we present a novel method to estimate the bearing of a passive tag relative to a mobile robot equipped with RFID(More)
— External perception based on vision plays a critical role in developing improved and robust localization algorithms, as well as gaining important information about the vehicle and the terrain it is traversing. This paper presents two novel methods for rough terrain-mobile robots, using visual input. The first method consists of a stereovision algorithm(More)
Ground segmentation is critical for a mobile robot to successfully accomplish its tasks in challenging environments. In this paper, we propose a self-supervised radar-vision classification system that allows an autonomous vehicle, operating in natural terrains, to automatically construct online a visual model of the ground and perform accurate ground(More)
Autonomous driving is a challenging problem in mobile robotics, particularly when the domain is unstructured, as in an outdoor setting. In addition, field scenarios are often characterized by low visibility as well, due to changes in lighting conditions, weather phenomena including fog, rain, snow and hail, or the presence of dust clouds and smoke. Thus,(More)