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Integration of CNN into a Robotic Architecture to Build Semantic Maps of Indoor Environments
| Type of publication: | Inbook |
| Citation: | davfercha_IWANN_2019_integration |
| Series: | Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science |
| Volume: | 11507 |
| Year: | 2019 |
| Month: | {{may}} |
| Pages: | 313--324 |
| Publisher: | Springer, Cham |
| ISBN: | 978-3-030-20517-1 |
| Key (?): | Semantic map, CNN, Object detection, YOLO, Unity 3D, Robotic architecture, Robot@Home, ROS |
| URL: | http://https://drive.google.co... |
| 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. |
| Userfields: | img_url=http%3A%2F%2Fdrive.google.com%2Fuc%3Fexport%3Dview%26id%3D1z-fgNZjndGTWjWgsNlI71X-CnNz_a8bD,rank_indexname=,rank_pos_in_category=,rank_num_in_category=,rank_cat_name=,impact_factor= |
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| Authors | |
| Added by: | [] |
| Total mark: | 0 |
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