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@INBOOK{davfercha_IWANN_2019_integration,
     author = {Fernandez-Chaves, David and Ruiz-Sarmiento, J. R. and Petkov, Nicolai and Gonzalez-Jimenez, Javier},
        key = {Semantic map, CNN, Object detection, YOLO, Unity 3D, Robotic architecture, Robot@Home, ROS},
      month = {{{may}}},
      title = {Integration of CNN into a Robotic Architecture to Build Semantic Maps of Indoor Environments},
     series = {Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science},
     volume = {11507},
       year = {2019},
  publisher = {Springer, Cham},
       isbn = {978-3-030-20517-1},
        url = {https://drive.google.com/uc?id=1ed-sxK8OPM7fG1rwqp_y0GGKxBC8DktF&export=download&authuser=0},
        doi = {10.1007/978-3-030-20518-8_27},
   abstract = {In robotics, semantic mapping refers to the construction ofa rich representation of the environment that includes high level infor-mation needed by the robot to accomplish its tasks. Building a semanticmap requires algorithms to process sensor data at different levels: geo-metric, topological and object detections/categories, which must be inte-grated into an unified model. This paper describes a robotic architecturethat successfully builds such semantic maps for indoor environments. Forthis purpose, within a ROS-based ecosystem, we apply a state-of-the-artConvolutional Neural Network (CNN), concretely YOLOv3, for detect-ing objects in images. The detection results are placed within a geometricmap of the environment making use of a number of modules of the ar-chitecture: robot localization, camera extrinsic calibration, data form adepth camera, etc. We demonstrate the suitability of the proposed frame-work by building semantic maps of several home environments from theRobot@Home dataset, using Unity 3D as a tool to visualize the maps aswell as to provide future robotic developments.},
      pages = {313--324}
}