This paper reports on AnnieWAY, an autonomous vehicle that is capable of driving through urban scenarios and that successfully entered the finals of the 2007 DARPA Urban Challenge competition. After describing the main challenges imposed and the major hardware components, we outline the underlying software structure and focus on selected algorithms. Environmental perception mainly relies on a recent laser scanner that delivers both range and reflectivity measurements. Whereas range measurements are used to provide three-dimensional scene geometry, measuring reflectivity allows for robust lane marker detection. Mission and maneuver planning is conducted using a hierarchical state machine that generates behavior in accordance with California traffic laws. We conclude with a report of the results achieved during the competition. C 2008 Wiley Periodicals, Inc.

We present rosbridge, a middleware abstraction layer which provides robotics technology with a standard, minimalist applications development framework accessible to applications programmers who are not themselves roboticists. Rosbridge provides a simple, socket-based programmatic access to robot interfaces and algorithms provided (for now) by ROS, the open-source "Robot Operating System", the current state-of-the-art in robot middleware. In particular, it facilitates the use of web technologies such as Javascript for the purpose of broadening the use and usefulness of robotic technology. We demonstrate potential applications in the interface design, education, human-robot interaction and remote laboratory environments.

This paper reports on AnnieWAY, an autonomous vehicle that is capable of driving through urban scenarios and that has successfully entered the finals of the DARPA Urban Challenge 2007 competition. After describing the main challenges imposed and the major hardware components, we outline the underlying software structure and focus on selected algorithms. Environmental perception mainly relies on a recent laser scanner which delivers both range and reflectivity measurements. While range measurements are used to provide 3D scene geometry, measuring reflectivity allows for robust lane marker detection. Mission and maneuver planning is conducted via a concurrent hierarchical state machine that generates behavior in accordance with California traffic laws. We conclude with a report of the results achieved during the competition.

Abstract-In this article we investigate the representation and acquisition of Semantic Objects Maps (SOMs) that can serve as information resources for autonomous service robots performing everyday manipulation tasks in kitchen environments. These maps provide the robot with information about its operation environment that enable it to perform fetch and place tasks more efficiently and reliably. To this end, the semantic object maps can answer queries such as the following ones: "What do parts of the kitchen look like?", "How can a container be opened and closed?", "Where do objects of daily use belong?", "What is inside of cupboards/drawers?", etc.The semantic object maps presented in this article, which we call SOM + , extend the first generation of SOMs presented by Rusu et al. [1] in that the representation of SOM + is designed more thoroughly and that SOM + also include knowledge about the appearance and articulation of furniture objects. Also, the acquisition methods for SOM + substantially advance those developed in [1] in that SOM + are acquired autonomously and with low-cost (Kinect) instead of very accurate (laser-based) 3D sensors. In addition, perception methods are more general and are demonstrated to work in different kitchen environments. I. INTRODUCTIONRobots that do not know where objects are have to search for them. Robots that do not know how objects look have to guess whether they have fetched the right one. Robots that do not know the articulation models of drawers and cupboards have to open them very carefully in order to not damage them. Thus, robots should store and maintain knowledge about their environment that enables them to perform their tasks more reliably and efficiently. We call the collection of this knowledge the robot's maps and consider maps to be models of the robot's operation environment that serve as information resources for better task performance. Robots build environment maps for many purposes. Most robot maps so far have been proposed for navigation. Robot maps for navigation enable robots to estimate their position in the environment, to check the reachability of the destination and to compute navigation plans. Depending on their purpose maps have to store different kinds of information in different forms. Maps might represent the occupancy of environment of 2D or 3D grid cells, they might contain landmarks or represent the topological structure of the environment. The maps might model objects of daily use, indoor, outdoor, underwater, extraterrestrial surfaces, and aerial environments.

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