{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/74984"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/74984","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"Environment Mapping in Larger Spaces","abstract":"Spatial mapping or environment mapping is the process of exploring a real world environment and creating its digital representation. To create convincing mixed reality programs, an environment mapping device must be able to detect a user's position and map the user's environment. Currently available commercial spatial mapping devices mostly use infrared camera to obtain a depth map which is effective only for short to medium distances (3-4 meters). This work describes an extension to the existing environment mapping devices and techniques to enable mapping of larger architectural environments using a combination of a camera, Inertial Measurement Unit (IMU), and Light Detection and Ranging (LIDAR) devices supported by sensor fusion and computer vision techniques. There are three main parts to the proposed system. The first part is data collection and data fusion using embedded hardware, the second part is data processing (segmentation) and the third part is creating a geometry mesh of the environment. The developed system was evaluated against its ability to determine the dimension of the room and of objects within the room. This low cost system can significantly expand the mapping range of the existing mixed reality devices such as Microsoft HoloLens device.","abstract_html":"Spatial mapping or environment mapping is the process of exploring a real world environment and creating its digital representation. To create convincing mixed reality programs, an environment mapping device must be able to detect a user's position and map the user's environment. Currently available commercial spatial mapping devices mostly use infrared camera to obtain a depth map which is effective only for short to medium distances (3-4 meters). This work describes an extension to the existing environment mapping devices and techniques to enable mapping of larger architectural environments using a combination of a camera, Inertial Measurement Unit (IMU), and Light Detection and Ranging (LIDAR) devices supported by sensor fusion and computer vision techniques. There are three main parts to the proposed system. The first part is data collection and data fusion using embedded hardware, the second part is data processing (segmentation) and the third part is creating a geometry mesh of the environment. 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