Carrying out a task by a team of mobile robots that move across an unknown environment is one of the open research lines with a higher scope for a large development in the mid-term. In order to accomplish this task it has been proved necessary to possess a highly detailed map of the environment that will allow the localization of the robots as they execute a particular task. During the last years the proposer research team has worked with remarkable results in the field of SLAM (Simultaneous Localization and Mapping) with teams of mobile robots. The work has considered the use of robots equipped with cameras and the inclusion of the visual information gathered in order to build map models. So far, different kind of maps have been built, including metric maps based on visual landmarks, as well as topological maps base on global appearance-based information extracted from images.
These maps have allowed the navigation of the robots in these maps as well as the performance of high level tasks in the environment. Nonetheless, there exists space for improvement in several areas related to the research carried out so far. Currently, one of the important problems consists in the treatment of the visual information and the updating of this information as the environment changes gradually. In addition, the maps should be created considering the dynamic and static part of the environment (for example when other mobile robots or people move in the environment), thus leading to the creation of more realistic models, as well as strategies to update the maps as changes are detected. A different research line considers the creation of maps that combine simultaneously the information about the topology of the environment, as well as semantic and metric information that will allow a more effective localization of the robot in large environments and, in addition, will enable a hierarchical localization in these maps. The proposed research project considers to tackle the aforementioned lines, thus considering the task of developing dynamic visual maps that will incorporate the semantic and topological structure of the environment, as well as the metric information when the robots perform trajectories with 6 degrees of freedom.