Mems based bridge monitoring supported by image-assisted total station

In this study, the feasibility of Micro-Electro-Mechanical System (MEMS) accelerometers and an image-assisted total station (IATS) for short-and long-term deformation monitoring of bridge structures is investigated. The MEMS sensors of type BNO055 from Bosch as part of a geo-sensor network are mounted at different positions of the bridge structure. In order to degrade the impact of systematic errors on the acceleration measurements, the deterministic calibration parameters are determined for fixed positions using a KUKA youBot in a climate chamber over certain temperature ranges. The measured acceleration data, with a sampling frequency of 100 Hz, yields accurate estimates of the modal parameters over short time intervals but suffer from accuracy degradation for absolute position estimates with time. To overcome this problem, video frames of a passive target, attached in the vicinity of one of the MEMS sensors, are captured from an embedded on-axis telescope camera of the IATS of type Leica Nova MS50 MultiStation with a practical sampling frequency of 10 Hz. To identify the modal parameters such as eigenfrequencies and modal damping for both acceleration and displacement time series, a damped harmonic oscillation model is employed together with an autoregressive (AR) model of coloured measurement noise. The AR model is solved by means of a generalized expectation maximization (GEM) algorithm. Subsequently, the estimated model parameters from the IATS are used for coordinate updates of the MEMS sensor within a Kalman filter approach. The experiment was performed for a synthetic bridge and the analysis shows an accuracy level of sub-millimetre for amplitudes and much better than 0.1 Hz for the frequencies. © 2019 M. Omidalizarandi et al

3D OBJECT COORDINATES EXTRACTION BY RADARGRAMMETRY AND MULTI STEP IMAGE MATCHING

Nowadays by high resolution SAR imaging systems as Radarsat-2, TerraSAR-X and COSMO-skyMed, three-dimensional terrain data extraction using SAR images is growing. InSAR and Radargrammetry are two most common approaches for removing 3D object coordinate from SAR images. Research has shown that extraction of terrain elevation data using satellite repeat pass interferometry SAR technique due to atmospheric factors and the lack of coherence between the images in areas with dense vegetation cover is a problematic. So the use of Radargrammetry technique can be effective. Generally height derived method by Radargrammetry consists of two stages: Images matching and space intersection. In this paper we propose a multi-stage algorithm founded on the combination of feature based and area based image matching. Then the RPCs that calculate for each images use for extracting 3D coordinate in matched points. At the end, the coordinates calculating that compare with coordinates extracted from 1 meters DEM. The results show root mean square errors for 360 points are 3.09 meters. We use a pair of spotlight TerraSAR-X images from JAM (IRAN) in this article

USING MULTI RESOLUTION CENSUS AND RANKLET TRANSFORMATION IN LONG BASE LINE SAR IMAGE MATCHING

Stereo radargrammetry is a mature technique for deriving height information from SAR image pairs. Generally height derived method by Radargrammetry consists of two stages: Images matching and space intersection. In this paper we propose a multi-step image matching algorithm founded on feature based matching. In this multi step algorithm, a SAR image is firstly filtered by a speckle suppression algorithm. a SIFT (Scale invariant feature transform) is used to extract feature points. Then we use non parametric Transformation as discriptor for the points extracted. Matching is sometimes more efficient when operating on image signals that have been transformed in some way, rather than operating on the pure intensity values themselves; In this article we use a pair of spotlight long base line TerraSAR-X images from JAM (IRAN). In a part with 700 × 700 pixels of these images 90 points are matched with using Ranklet algorithm. The mean absolute error of the corresponding points is 0.9 pixel. This match points number is 49 points with using multi resolution Census. The result shows that our proposed multi step image matching is superior to the Most FBM methods in terms of accuracy and number of matched points

CAMERA PLACEMENT FOR NETWORK DESIGN IN VISION METROLOGY BASED ON FUZZY INFERENCE SYSTEM

For measuring complex industrial objects using vision metrology systems, automatic optimum network design is a real challenge. In the absence of given or simulated 3D CAD models of the objects and the workspace, the complexity of objects introduces several uncertainty factors into the camera placement decision making process. These uncertainty factors include the vision constraints such as visibility, accessibility and camera-object distance. For more complex objects, visibility is vastly influenced by hidden areas, the incidence angle of a target and the camera orientation. Mutual dependency of these factors increases the difficulty of camera placement. Further these factors directly influence the mensuration quality, in particular, precision and reliability. If an a priori 3D CAD model of the object is available, the aforementioned ambiguities can be tackled. However, a 3D model is often not available which makes the camera placement problem a nondeterministic process. An answer to this problem is to develop a fuzzy logic inference approach for camera placement and network design. The idea is to deal with the vision constraints in a fuzzy manner. In this paper a novel method based on fuzzy logic reasoning strategy is proposed for the camera placement. The system is designed to make use of human type reasoning strategy by incorporating appropriate rules.The paper reports on the results achieved by testing the fuzzy based camera placement approach on simulated and real objects. The results indicate that this new conceptual approach has a remarkable strength for automatic sensor placement in vision metrology. 1