Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Structural displacement estimation by fusing vision camera and accelerometer using hybrid computer vision algorithm and adaptive multi-rate Kalman filter
Abstract Structural displacement monitoring is essential because displacement can provide critical information regarding the health condition of civil structures. However, the precise estimation of structural displacement remains a challenge. This paper describes a displacement estimation technique that fuses asynchronous acceleration and vision measurements at different sampling rates. A hybrid computer vision (CV) algorithm and an adaptive multi-rate Kalman filter are integrated to efficiently estimate high-sampling displacement from low-sampling vision measurement and high-sampling acceleration measurement. An initial calibration algorithm is proposed to automatically determine active pixels and two scale factors required in the hybrid CV algorithm without any prior knowledge or ad-hoc thresholding. The proposed technique was experimentally validated and high-sampling displacements were accurately estimated in real-time with less than 1.5 mm error, indicating the potential of the proposed technique for practical applications in long-term continuous structural displacement monitoring.
Highlights A hybrid CV algorithm is proposed by combining the FM and POF algorithms. Two scale factors for converting phase and translation to displacement, respectively, are automatically estimated. Active pixels within an ROI are automatically selected without any ad-hoc threshold. Displacement estimation accuracy is significantly improved compared to previous techniques in laboratory and field tests.
Structural displacement estimation by fusing vision camera and accelerometer using hybrid computer vision algorithm and adaptive multi-rate Kalman filter
Abstract Structural displacement monitoring is essential because displacement can provide critical information regarding the health condition of civil structures. However, the precise estimation of structural displacement remains a challenge. This paper describes a displacement estimation technique that fuses asynchronous acceleration and vision measurements at different sampling rates. A hybrid computer vision (CV) algorithm and an adaptive multi-rate Kalman filter are integrated to efficiently estimate high-sampling displacement from low-sampling vision measurement and high-sampling acceleration measurement. An initial calibration algorithm is proposed to automatically determine active pixels and two scale factors required in the hybrid CV algorithm without any prior knowledge or ad-hoc thresholding. The proposed technique was experimentally validated and high-sampling displacements were accurately estimated in real-time with less than 1.5 mm error, indicating the potential of the proposed technique for practical applications in long-term continuous structural displacement monitoring.
Highlights A hybrid CV algorithm is proposed by combining the FM and POF algorithms. Two scale factors for converting phase and translation to displacement, respectively, are automatically estimated. Active pixels within an ROI are automatically selected without any ad-hoc threshold. Displacement estimation accuracy is significantly improved compared to previous techniques in laboratory and field tests.
Structural displacement estimation by fusing vision camera and accelerometer using hybrid computer vision algorithm and adaptive multi-rate Kalman filter
Ma, Zhanxiong (Autor:in) / Choi, Jaemook (Autor:in) / Liu, Peipei (Autor:in) / Sohn, Hoon (Autor:in)
06.05.2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Bridge displacement estimation by fusing accelerometer and strain gauge measurements
| Wiley | 2021