{"id":1546,"date":"2020-12-15T08:36:00","date_gmt":"2020-12-15T08:36:00","guid":{"rendered":"https:\/\/mapir.isa.uma.es\/mapirwebsite_wordpress\/?p=1546"},"modified":"2024-02-06T14:52:14","modified_gmt":"2024-02-06T14:52:14","slug":"wiser-building-and-exploiting-semantic-maps-by-mobile-robots","status":"publish","type":"post","link":"https:\/\/mapir.isa.uma.es\/mapirwebsite\/?p=1546","title":{"rendered":"WISER: Building and exploiting semantic maps by mobile robots
(Jun’18\u2013Dec’20)
National Project<\/div>"},"content":{"rendered":"\n

The purpose of project is to provide the robot with the capability to understand the environment by building object-based maps (i.e. semantic maps) that can latter be used to carry out high-level tasks. Thus, it will work on semantic SLAM with the focus on different challenging, open issues.<\/p>\n\n\n\n\n\n\n\n

The recognition of objects from the environment is an esential task for a robot to perform intelligently and for interacting with people. In real conditions this becomes a very dificult problem due to the number of possible object classes, as well as the poor adquisition conditions that the robot may encounter: occlusions, changing lighting conditions, very different apperances for 3D objects, etc. To face the complexity of the problem, we can take advantage of the possibilities that a robot offers, including active perception (i.e, to plan viewpoints that enhance the objetc recognition), and to use all the information that it may have about the workspace, like a map built previously.<\/p>\n\n\n\n

To create an useful object-based model of the environment, not only the robot needs to recognize objects, but to compute their poses in the world, that is, with respect to itself while keeping a perfect self-localization. Moreover, these objects must be linked to relevant information like their properties and functionality, commonly called semantic information.<\/p>\n\n\n\n

The purpose of this project is to advance in this direction by providing the robot with the capability to understand the environment by building object-based maps (i.e. semantic maps) that can latter be used to carry out high-level tasks<\/strong>. Thus, we will work on semantic SLAM (Simultaneous localization and mapping) with the focus on:<\/p>\n\n\n\n

  1. Developing algorithms for object recognition and pose estimation, particularly by resorting on CNN (Convolutional Neural Networks).<\/li>
  2. The integration and maintainance over time of the semantic information along with the topological, the geometrical and the appearance.<\/li>
  3. The investigation of mechanisms for active perception to overcome occlusion and ambiguity situations.<\/li>
  4. The exploitation of the semantic maps for assisting people (e.g. finding objects).<\/li><\/ol>\n\n\n\n

    The sensorial system to be used comprises one or several RGB-D cameras onboard the robot, thus effective and quick autocalibration procedures will be developed.<\/p>\n\n\n\n

    REFERENCE:\u00a0<\/strong>DPI2017-84827-R
    FUNDED BY:\u00a0<\/strong>Ministry of Economy and Competitiveness
    PERIOD:\u00a0<\/strong>Jan 2018 \u2013 Dec 2020
    PRINCIPAL RESEARCHER:\u00a0<\/strong>JAVIER GONZ\u00c1LEZ-JIM\u00c9NEZ<\/a>
    INSTITUTION:\u00a0<\/strong>University of M\u00e1laga<\/p><\/blockquote>\n\n\n\n

    Publications<\/strong><\/p>\n\n\n\n

    Publications in the scope of this project:<\/p>\n\n\n\n