Jaime Pulido Fentanes

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This paper presents a new approach to mobile robot mapping in long-term scenarios. So far, the environment models used in mobile robotics have been tailored to capture static scenes and dealt with the environment changes by means of `memory decay'. While these models keep up with slowly changing environments, their utilization in dynamic, real world(More)
This paper presents a new approach for topological localisation of service robots in dynamic indoor environments. In contrast to typical localisation approaches that rely mainly on static parts of the environment, our approach makes explicit use of information about changes by learning and modelling the spatio-temporal dynamics of the environment where the(More)
In planning for deliberation or navigation in real-world robotic systems, one of the big challenges is to cope with change. It lies in the nature of planning that it has to make assumptions about the future state of the world, and the robot's chances of successively accomplishing actions in this future. Hence, a robot's plan can only be as good as its(More)
Environments Nick Hawes†, Chris Burbridge, Ferdian Jovan, Lars Kunze, Bruno Lacerda, Lenka Mudrová, Jay Young, Jeremy Wyatta Denise Hebesberger, Tobias Körtnerb Rares Ambrus, Nils Bore, John Folkesson, Patric Jensfeltc Lucas Beyer, Alexander Hermans, Bastian Leibed Aitor Aldoma, Thomas Fäulhammer, Michael Zillich, Markus Vinczee Eris Chinellatof Muhannad(More)
In this paper, an algorithm for the reconstruction of an outdoor environment using a mobile robot is presented. The focus of this algorithm is making the mapping process efficient by capturing the greatest amount of information on every scan, ensuring at the same time that the overall quality of the resulting 3D model of the environment complies with the(More)
This letter presents an exploration method that allows mobile robots to build and maintain spatio-temporal models of changing environments. The assumption of a perpetually changing world adds a temporal dimension to the exploration problem, making spatio-temporal exploration a never-ending, life-long learning process. We address the problem by application(More)
This paper describes the motivation and learning subsystems of Arisco which is a mechatronic head with interactive capacity which includes high expressivity through gesturing, voice recognition, text to speech generation, visual tracking, and Internet information retrieval. The general architecture is first described in the paper. Then, the learning(More)
We present a lifelong mapping and localisation system for long-term autonomous operation of mobile robots in changing environments. The core of the system is a spatio-temporal occupancy grid that explicitly represents the persistence and periodicity of the individual cells and can predict the probability of their occupancy in the future. During navigation,(More)
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