Visual–inertial structural acceleration measurement

Abstract

Structural vibration measurement is a crucial and necessary step for structural health monitoring. Recently, computer vision-based techniques have been proposed by researchers to measure structural motion remotely. However, the direct application of vision-based measurement to practical applications still faces some challenges, mainly because intrinsic camera vibration can introduce significant errors to the measurement results. In this study, a three-stage approach using an embedded inertial measurement unit is proposed to compensate for the camera movement. First, camera rotations are estimated by employing a complementary filter with an adaptive gain to fuse gyroscope measurement and accelerometer data. Next, binary robust invariant scalable key-point features are detected from the region of interest and tracked between video frames using a Kanade-Lucas-Tomasi tracker. Finally, structural acceleration is obtained by combining the information for the obtained structural features and the estimated nonstationary camera motion. The performance of the proposed approach is investigated using both a moving handheld camera and a camera mounted on the unmanned aerial vehicle in the laboratory. These results demonstrate that the proposed method can be effectively applied to measure structural vibration, without requiring stationary background features to be available in the video.

Computer Vision and Image Processing in Structural Health Monitoring: Overview of Recent Applications

“…The analysis revealed that several sensors had been used for validation purposes, including laser displacement sensors [32,34,41,42,49,70,74,76,82], accelerometers [32][33][34]50,51,53,55,59,61,[64][65][66][67][68]76], IMU [60], linear variable differential transformers [34,36,41,50,54,61,66,67,73], laser Doppler vibrometers [38,46], stationary cameras [42,75,83], actuators [71], laser trackers [81], infrared sensors [48], and marker-based systems [44,73]. Marker-based systems are routinely used in other fields (such as medicine), where they are the traditional reference systems for motion capture and analysis.…”

“…This choice applies to the following studies: [34,[40][41][42]44,45,[48][49][50][51][53][54][55][58][59][60][61][62][63][64]66,67,[69][70][71][72][73][74][75][76][77][78][80][81][82]. In a few cases, the experimental scenarios were outdoors where, on the other hand, it was necessary to consider, at least in part, the environmental conditions and the problems previously indicated, testing the solution on scale models in most cases as well.…”

Displacement measurement of large structures using nonoverlapping field of view multi‐camera systems under six degrees of freedom ego‐motion

“…Weng et al. (2022) used a complementary filter with an adaptive gain to estimate the 3‐DOF camera rotations from the IMU measurements. Although this technique no longer needs to find the stationary points in the camera field of view (FOV), it has the disadvantages of high cost and an accumulative measurement error over time (Kok et al., 2017).…”

“…Ribeiro et al (2021) used certain numerical integration techniques to estimate the translations and rotations of the UAV from the data of acceleration and angular velocity recorded by accelerometers and gyroscopes, respectively. Weng et al (2022) used a complementary filter with an adaptive gain to estimate the 3-DOF camera rotations from the IMU measurements. Although this technique no longer needs to find the stationary points in the camera field of view (FOV), it has the disadvantages of high cost and an accumulative measurement error over time (Kok et al, 2017).…”