Measurement of 3-D Vibrational Motion by Dynamic Photogrammetry Using Least-Square Image Matching for Sub-Pixel Targeting to Improve Accuracy

Abstract

This paper deals with an improved methodology to measure three-dimensional dynamic displacements of a structure by digital close-range photogrammetry. A series of stereo images of a vibrating structure installed with targets are taken at specified intervals by using two daily-use cameras. A new methodology is proposed to accurately trace the spatial displacement of each target in three-dimensional space. This method combines the correlation and the least-square image matching so that the sub-pixel targeting can be obtained to increase the measurement accuracy. Collinearity and space resection theory are used to determine the interior and exterior orientation parameters. To verify the proposed method, experiments have been performed to measure displacements of a cantilevered beam excited by an electrodynamic shaker, which is vibrating in a complex configuration with mixed bending and torsional motions simultaneously with multiple frequencies. The results by the present method showed good agreement with the measurement by two laser displacement sensors. The proposed methodology only requires inexpensive daily-use cameras, and can remotely detect the dynamic displacement of a structure vibrating in a complex three-dimensional defection shape up to sub-pixel accuracy. It has abundant potential applications to various fields, e.g., remote vibration monitoring of an inaccessible or dangerous facility.

Deformation Measurement of a Railroad Bridge Using a Photogrammetric Board without Control Point Survey

“…, and Z 0i are the spatial coordinates of the camera lens center from a user-defined local coordinate system; X, Y, and Z are the spatial coordinates of targets on the structure; f i represents the focal lengths; and m i,11 , m i,12 , … , m i,33 are components of the rotation matrix based on rotational angles (ω, φ, and κ) with respect to the x-, y-, and z-axes, respectively. Finally, the 3D spatial coordinates (P) of the targets in each set of stereo images can be computed by the space intersection [6]. This is achieved using the image coordinates of the corresponding points (p 1 and p 2 ) of the left and right images with the IOPs and EOPs of the camera lens center (O 1 and O 2 ), as shown in Figure 3.…”

“…The precisions indicate that the parameters are overall well-determined in the solution of the least-squares method. To evaluate the actual accuracy of the determined parameters, the 3D coordinates of the check points for each case were computed using the space intersection equation [6]. The results were compared with the measured positions by the total station (case 1) and a certain feature space (case 2), as shown in Table 5.…”

“…This methodology enables full-field measurement of a structure because there is no limit in the number of targets. Moreover, when unique features on the structure surface are utilized, such as corners, the targets may not even be necessary [6].…”

Remote Sensing Image Matching Based Improved ORB in NSCT Domain

“…Image matching is the basic component of the field of machine vision, which is now widely used in many fields, such as medicine, agriculture, remote sensing, machinery and artificial intelligence, etc. (Uchiyama et al 2015;Schmid et al 2000;Reese et al 2015;Sedaghat and Ebadi 2015;Lee et al 2016;Ye et al 2017). For image matching, it is mainly divided into gray-based matching and featurebased matching (Moon and Loh 2015).…”

Dynamic Displacement of an Aluminum Frame Using Close Range Photogrammetry

“…To increase the accuracy of 3D measurements of a vibrating structure, Lee et al (2016) introduced the least-square image matching for sub-pixel targeting. In this method, a finer pixel spacing is created by determining sub-pixels based on the least square method of the matched pixels in two overlapping images [23].…”