This paper presents a fully autonomous navigation solution for urban, pedestrian environments. The task at hand, undertaken within the context of the European project URUS, was to enable two urban service robots, based on Segway RMP200 platforms and using planar lasers as primary sensors, to navigate around a known, large (10,000 m 2 ), pedestrian-only environment with poor global positioning system coverage. Special consideration is given to the nature of our robots, highly mobile but two-wheeled, self-balancing, and inherently unstable. Our approach allows us to tackle locations with large variations in height, featuring ramps and staircases, thanks to a three-dimensional, map-based particle filter for localization and to surface traversability inference for low-level navigation. This solution was tested in two different urban settings, the experimental zone devised for the project, a university campus, and a very crowded public avenue, both located in the city of Barcelona, Spain. Our results total more than 6 km of autonomous navigation, with a success rate on go-to requests of nearly 99%. The paper presents our system, examines its overall performance, and discusses the lessons learned throughout development. C 2011 Wiley Periodicals, Inc.

Abstract-Inner pipe inspection of sewer networks is a hard and tedious task, due to the nature of the environment, which is narrow, dark, wet and dirty. So, mobile robots can play an important role to solve condition assessment of such huge civil infrastructures, resulting in a clear benefit for citizens. One of the fundamental tasks that a mobile robot should solve is localization, but in such environments GPS signal is completely denied, so alternative methods have to be developed. Visual odometry and visual SLAM are promising techniques to be applied in such environments, but they require a populated set of visual feature tracks, which is a requirement that can not be fulfilled in such environments in a continuous way. With the aim of designing robust and reliable robot systems, this paper proposes and evaluates a complementary approach to localize a mobile robot, which is based on sensor data fusion of an inertial measurement unit and of a cable encoder, which measures the length of an unfolded cable, from the starting point of operations up to the tethered robot. Data fusion is based on optimization of a set of windowed states given the sensor measurements in that window. The paper details theoretical basis, practical implementation issues and results obtained in testing pipe scenarios.

Abstract-This paper presents an asynchronous particle filter algorithm for mobile robot position tracking, taking into account time considerations when integrating observations being delayed or advanced from the prior estiamate time point. The interest of that filter lies in cooperative environments and in fast vehicles. The paper studies the first case, where a sensor network shares perception data with running robots that receive accurate obeservations with large delays due to acquisition, processing and wireless communications. Promising simulated results comparing a basic particle filter and the proposed one are shown. The paper also investigates a situation where a robot is tracking its position, fusing only odometry and observations from a camera network partially covering the robot path.

Abstract-This paper presents a novel and efficient framework to the active map-based global localization problem for mobile robots operating in large and cooperative environments. The paper proposes a rational criteria to select the action that minimizes the expected number of remaining position hypotheses, for the single robot case and for the cooperative case, where the lost robot takes advantage of observations coming from a sensor network deployed on the environment or from other localized robots. Efficiency in time complexity is achieved thanks to reasoning in terms of the number of hypotheses instead of in terms of the belief function. Simulation results in a real outdoor environment of 10.000m2 are presented validating the presented approach and showing different behaviours for the single robot case and for the cooperative one.

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