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Three-dimensional structural displacement estimation by fusing monocular camera and accelerometer using adaptive multi-rate Kalman filter
Highlights Three-dimensional displacement estimation using monocular camera and accelerometer; Two-targets-based separation of in- and out-of-plane displacements; Automated parameter calibration for vision-based displacement estimation; Accurate out-of-plane displacement estimation at a long target-to-camera distance.
Abstract Structural displacements play an important role in the health monitoring of civil structures; however, the accurate measurement of structural displacements remains a difficult task. Previous efforts have combined a monocular camera and an accelerometer to estimate structural displacement, but only in-plane displacements could be estimated in this way. In this study, the fusion of a monocular camera and an accelerometer was further extended for out-of-plane or three-dimensional displacement estimation. A computer vision algorithm and an adaptive multi-rate Kalman filter were integrated to efficiently estimate high-sampled displacements from low-sampled vision images and high-sampled acceleration measurements. All parameters associated with the computer vision algorithm were automatically calibrated without using any user-defined thresholds. Experimental validation was performed on two building structures and a 10-m-long bridge structure, and the proposed method accurately estimated the displacement for all three structures with a root mean square error of less than 1 mm.
Three-dimensional structural displacement estimation by fusing monocular camera and accelerometer using adaptive multi-rate Kalman filter
Highlights Three-dimensional displacement estimation using monocular camera and accelerometer; Two-targets-based separation of in- and out-of-plane displacements; Automated parameter calibration for vision-based displacement estimation; Accurate out-of-plane displacement estimation at a long target-to-camera distance.
Abstract Structural displacements play an important role in the health monitoring of civil structures; however, the accurate measurement of structural displacements remains a difficult task. Previous efforts have combined a monocular camera and an accelerometer to estimate structural displacement, but only in-plane displacements could be estimated in this way. In this study, the fusion of a monocular camera and an accelerometer was further extended for out-of-plane or three-dimensional displacement estimation. A computer vision algorithm and an adaptive multi-rate Kalman filter were integrated to efficiently estimate high-sampled displacements from low-sampled vision images and high-sampled acceleration measurements. All parameters associated with the computer vision algorithm were automatically calibrated without using any user-defined thresholds. Experimental validation was performed on two building structures and a 10-m-long bridge structure, and the proposed method accurately estimated the displacement for all three structures with a root mean square error of less than 1 mm.
Three-dimensional structural displacement estimation by fusing monocular camera and accelerometer using adaptive multi-rate Kalman filter
Ma, Zhanxiong (author) / Choi, Jaemook (author) / Sohn, Hoon (author)
Engineering Structures ; 292
2023-06-28
Article (Journal)
Electronic Resource
English
Bridge displacement estimation by fusing accelerometer and strain gauge measurements
| Wiley | 2021