We propose an unsupervised probabilistic framework for learning a human-centred
representation of a person's environment from first-person video. Specifically, non-geometric …

In this paper, a hierarchical localization framework within indoor environments is proposed
and evaluated, considering severe variations of the illumination conditions. The only source …

This paper presents an extended study about the compression of topological models of
indoor environments. The performance of two clustering methods is tested in order to know …

In this work, an incremental clustering approach to obtain compact hierarchical models of an
environment is developed and evaluated. This process is performed using an …

Map building and localization are two important abilities that autonomous mobile robots
must develop. This way, much research has been carried out on these topics, and …

We consider unsupervised learning of semantic, user-specific maps from first-person video.
The task we address can be thought of as a semantic, non-geometric form of simultaneous …

Over the last few years, the presence of the mobile robotics has considerably increased in a
wide variety of environments. It is common to find robots that carry out repetitive and specific …

La localización y creación de mapas son dos tareas que los robots móviles autónomos
deben desarrollar. Por ello, se han llevado a cabo numerosas investigaciones sobre estos …