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  • 學位論文

應用投影式擴增實境於現地建築資訊擷取

Onsite Building Information Retrieval by Using Projection-Based Augmented Reality

指導教授 : 康仕仲

摘要


並列摘要


This research focuses on a long-lasting construction problem: onsite information retrieval. I have therefore developed a wearable device that can project the construction drawings and related information based on the needs of the users. This device avoids engineers carrying bulky construction drawings to the site and reduces the effort required in looking for the correct drawings to obtain the information they need. This device includes four modules: the information integration module, the display module, the positioning module and the manipulation module. The information integration module is used to transfer the information in the building information model (BIM) into images to enable the onsite retrieval from the device I developed. The position module enables users to input their locations and automatically search for the images that the users might need. The manipulation module can analyze the gestures of the users from the touch screen and acceleratometer in the devices, and then crop the images to eliminate the unneeded information. The display module, which directly links to the projector, can continually calculate the images processed by the previous three modules and scale the images accordingly, ensuring that the projection results in a correct scale. I also developed a hardware device, coined the iHelmet, to implement the four modules. It consists of a construction helmet (weight: 460g), an iPod Touch (weight: 115g) and an Optoma LED projector (Weight: 114g). To validate the usability of the iHelmet onsite, I conducted a user test with 34 participants. I compared the efficiency and effectiveness of retrieving building information using the iHelmet with using the traditional 2D drawings approach. The results showed that the mean completion times were significantly shorter for participants using the iHelmet (iHelmet: 44 seconds, 3 traditional approach: 99 seconds). The mean success rates of participants arriving at the correct answers were also significantly improved for those using the iHelmet (iHelmet: 91.6%, traditional approach: 64.3%).

參考文獻


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