Three-dimensional structural displacement estimation by fusing monocular camera and accelerometer using adaptive multi-rate Kalman filter

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.