Deflection measurement is the research focus of health monitoring for bridges during the operation period. This study develops a contactless measurement technique to monitor the bridge deflection, leveraging visual information from a team of unmanned aerial vehicles (UAVs). On the basis of the collinearity of the laser spots projected on the plane by the coplanar laser indicator, we can eliminate the motion of UAV, and calculate the vertical displacement of the position to be measured relative to the bridge pier. In the proposed method, the center of the laser spot is extracted through a method based on deep learning, and an algorithm based on scale-invariant features registration was developed to track the feature points of the bridge in the image sequence. According to the algorithm, we demonstrate the accuracy and feasibility of our approach through simulation and simulated bridge experiments. The result shows that the root mean squared error (RMSE) of measurement through our technique is less than 0.5 mm in the laboratory conditions. In addition, the limits and scalability of the presented method have been explored through a field experiment.

The registration of astronomical images, is one of the key technologies to improve the detection accuracy of small and weak targets during astronomical image-based observation. The error of registration has a great influence on the trace association of targets. However, most of the existing methods for point-matching and image transformation lack pertinence for this actual scene. In this study, we propose a registration algorithm based on geometric constraints and homography, for astronomical images. First, the position changes in stars in an image caused by the motion of the platform where the camera had been stationed, were studied, to choose a more targeted registration model, which is based on homography transformation. Next, each image was divided into regions, and the enclosed stable stars were used for the construction of triangles, to reduce the errors from unevenly distributed points and the number of triangles. Then, the triangles in the same region of two images were matched by the geometric constraints of side lengths and a new cumulative confidence matrix. Finally, a strategy of two-stage estimation was applied, to eliminate the influence of false pairs and realize accurate registration. The proposed method was then tested on sequences of real optical images under different imaging conditions and confirmed to have outstanding performance in the dispersion rate of points, the accuracy of matching, and the error of registration, as compared to baseline methods. The mean pixel errors after registration for different sequences are all less than 0.5 when the approximate rotation angle per image is from 0.58 $\times {10}^{-2}$ to 5.89 $\times {10}^{-2}$.

Capturing the motion of humans without any markers in a moving scene is a challenging issue. Typical methods, such as those based on fixed cameras or wearable sensors, have limits on the measurement range and the number of targets. Therefore, we propose a markerless motion capture technique to estimate the 3D pose of humans through a multi-UAV system. By utilizing the advantage of the large observation baselines between UAVs in the system, an algorithm to estimate the pose of airborne cameras and establish the sparse points cloud of background in overlapping view simultaneously was presented. A 3σ rule based on reprojection error is employed to eliminate the incorrect conjugate keypoint pairs, enhancing the precision of pose estimation for airborne cameras. To verify the accuracy of the developed technique, an indoor test was conducted to compare it with a commercial capture system, the results show that the average measurement error of the 3D joints of humans is less than 30mm. Furthermore, we demonstrate the feasibility of our approach in outdoor scenes, including multi-person scenarios and moving scenarios.

During astronomical observation, the centroiding of space targets is a crucial technology for improving trajectory association in images, celestial positioning, and orbit determination. However, when the exposure time is short, the distribution of targets and stars in different regions of an image can vary significantly, seriously affecting centroiding accuracy. Motivated by the relationship between target distribution and the corresponding optical system, we propose a centroiding method based on local point spread function (PSF) estimation and iterative deconvolution. First, the imaging process of an ideal target is analyzed to identify key factors affecting the distribution of various targets. Next, the local PSF around a target is estimated using the distribution of nearby stars. Then, iterative deconvolution is used to reconstruct the region of interest (ROI) around the target with the estimated local PSF. Finally, an improved weighted centroid is calculated using a restricted region growing method. The proposed method was tested on simulation experiments and multiple sequences of collected real optical images with stars and targets in different regions. The results showed outstanding performance in terms of mean absolute errors, standard deviation of star location, and movement fluctuations compared to baseline methods. The mean absolute errors for centroiding in Gaussian noise with a standard deviation of less than 0.02 and displacement within 1 pixel are 0.16 and 0.06 pixels, respectively.

The temporal and spatial relationships among various onboard sensors are crucial for ensuring the accuracy of visual observations carried out by the multiple unmanned aerial vehicles (multi-UAV) system. To leverage the integration within the multi-UAV system, we propose a buddy calibration method focused on calibrating the relative time offset and rotation parameters between the airborne camera and flight controller. We designate another UAV as the target and perform 6D pose estimation to calculate the relative pose between the camera mounted on the host UAV and the target UAV. Subsequently, we use the pose estimation results and the parameters recorded by the sensors on both UAVs to establish a comprehensive temporal and spatial calibration model. Finally, a specific method is developed to effectively solve the proposed model. Our approach was validated using two-UAV flight experiments. The results demonstrate a significant improvement, with leader attitude estimation errors decreasing to approximately less than 1 $^{\circ }$ post-calibration compared to pre-calibration. Furthermore, positioning errors for dynamic and static targets were substantially reduced over 42.59% and 53.36%, respectively.