Self-corrective knowledge-based hybrid tracking system using BIM and multimodal sensors. Issue 2 (April 2017)

• Record Type: Journal Article
• Authors: Park, JeeWoong; Chen, Jingdao; Cho, Yong K.
• Is Part Of: Advanced engineering informatics. Volume 32:Issue 2(2017:Apr.)
• Page Start: 126
• Page End: 138
• Publication Date: 2017-04
• Subjects: Bluetooth Low Energy (BLE) -- Building Information Model (BIM) -- Inertial Measurement Units (IMU) -- Location tracking -- Mobile sensing -- Data fusion; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2017.02.001
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: We propose a self-corrective real-time tracking system using a hybrid approach with BLE, motion sensors, and BIM. A full-site construction test was conducted to validate the developed hybrid tracking system. The proposed hybrid tracking system presented a way to compensate for the weakness of each system component. The system components show positive effects through the interaction by reducing tracking errors up to 42%. Abstract: Researchers have recently devoted considerable attention to acquiring location awareness of assets. They have explored various technologies, such as video cameras, radio signal strength indicator-based sensors, and motion sensors, in the development of tracking systems. However, each system presents unique drawbacks especially when applied in complex indoor construction environments; this paper classifies them into two categories: absolute tracking and relative tracking. By understanding the nature of problems in each tracking category, this research develops a novel tracking methodology that uses knowledge of the strengths and weaknesses of various components used in the proposed tracking system. This paper presents the development of a hybrid-tracking system that integrates Bluetooth Low Energy (BLE) technology, motion sensors, and Building Information Model (BIM). The hypothesis tested through this integration was whether such knowledge-based integration could provide a method that can correct errors found in each of the used sensing technologies and thereby improve the reliability of the tracking system. Field experimental trials were conducted in a full-scale indoor construction site to assess the performance of individual components and the integrated system. The results indicated that the addition of map knowledge from a BIM model showed the capability of correcting improbable movements. Furthermore, the knowledge-based decision making process demonstrated its capability to make positive interaction by reducing the positioning errors by 42% on average. In sum, the proposed hybrid-tracking system presented a novel method to compensate for the weakness of each system component and thus achieve a more accurate and precise tracking in dynamic and complex indoor construction sites.