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Visual–inertial structural acceleration measurement
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.
Visual–inertial structural acceleration measurement
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.
Visual–inertial structural acceleration measurement
Weng, Yufeng (author) / Lu, Zheng (author) / Lu, Xilin (author) / Spencer, Billie F. Jr. (author)
Computer‐Aided Civil and Infrastructure Engineering ; 37 ; 1146-1159
2022-07-01
14 pages
Article (Journal)
Electronic Resource
English
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