Virtual sensors for human concepts—Building detection by an outdoor mobile robot |
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| Affiliation: | 1. Center for Applied Autonomous Sensor Systems, Department of Technology, Örebro University, Örebro, Sweden;2. Department of Computing and Informatics, University of Lincoln, Lincoln, UK;1. State Key Laboratory of Geodesy and Earth’s Dynamics, Institute of Geodesy and Geophysics, Chinese Academy of Sciences, Wuhan, China;2. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China;3. The First Institute of Oceanography, SOA, Qingdao, China;1. Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China;2. Unit 61175 of PLA, Nanjing 210049, China;3. Department of Geography, National University of Singapore, Singapore;1. Department of Graduate Medical Education, MedStar Georgetown University Hospital, Washington, District of Columbia;2. Medical Oncology Service Bradford Hill, Santiago de Chile, Chile;3. Intensive Care Unit, Clinica Alemana, Santiago, Chile;4. Medical Oncology Service, Clinica Universidad de los Andes, Las Condes, Chile;5. Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, California;6. Georgetown Lombardi Comprehensive Cancer Center, MedStar Georgetown University Hospital, Washington, District of Columbia;1. Spatial Data Mining and Application Research Center of Fujian Province, Yango University, Fuzhou 350015, China;2. Information Engineering College, Yango University, Fuzhou 350015, China;3. College of Computing and Informatics, University of North Carolina at Charlotte, Charlotte, NC 28223, USA;1. School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, Hubei 430074, PR China;2. Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei 430074, PR China;3. State Key Laboratory of Rare Earth Resource Utilization, Changchun Institute of Applied Chemistry, Jilin, Changchun 130022, PR China |
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| Abstract: | In human–robot communication it is often important to relate robot sensor readings to concepts used by humans. We suggest the use of a virtual sensor (one or several physical sensors with a dedicated signal processing unit for the recognition of real world concepts) and a method with which the virtual sensor can learn from a set of generic features. The virtual sensor robustly establishes the link between sensor data and a particular human concept. In this work, we present a virtual sensor for building detection that uses vision and machine learning to classify the image content in a particular direction as representing buildings or non-buildings. The virtual sensor is trained on a diverse set of image data, using features extracted from grey level images. The features are based on edge orientation, the configurations of these edges, and on grey level clustering. To combine these features, the AdaBoost algorithm is applied. Our experiments with an outdoor mobile robot show that the method is able to separate buildings from nature with a high classification rate, and to extrapolate well to images collected under different conditions. Finally, the virtual sensor is applied on the mobile robot, combining its classifications of sub-images from a panoramic view with spatial information (in the form of location and orientation of the robot) in order to communicate the likely locations of buildings to a remote human operator. |
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