With extensive application of RGB-D cameras in robotics, computer vision, and many other fields, accurate calibration becomes more and more critical to the sensors. However, most existing models for calibrating depth and the relative pose between a depth camera and an RGB camera are not universally applicable to many different kinds of RGB-D cameras. In this paper, by using the collinear equation and space resection of photogrammetry, we present a new model to correct the depth and calibrate the relative pose between depth and RGB cameras based on a 3D control field. We establish a rigorous relationship model between the two cameras; then, we optimize the relative parameters of two cameras by least-squares iteration. For depth correction, based on the extrinsic parameters related to object space, the reference depths are calculated by using a collinear equation. Then, we calibrate the depth measurements with consideration of the distortion of pixels in depth images. We apply Kinect-2 to verify the calibration parameters by registering depth and color images. We test the effect of depth correction based on 3D reconstruction. Compared to the registration results from a state-of-the-art calibration model, the registration results obtained with our calibration parameters improve dramatically. Likewise, the performances of 3D reconstruction demonstrate obvious improvements after depth correction.

Deformation measurements are repeatable. Therefore, they are often performed with the same equipment, methods, and geometric conditions in different epochs. However, although the measuring environment might be the same in different epochs, it sometimes changes significantly. Of all environmental parameters (atmospheric pressure, temperature, etc.), the atmospheric refraction coefficient is the most significant. Because it is unstable and changeable, measurement accuracy is affected. To solve this issue, an inversion method of the atmospheric refraction coefficient based on a trigonometric leveling network is proposed. This method is based on a mathematical model of the atmospheric refraction coefficient with weighted values, which are determined according to the difference between direct and reversed elevations. Therefore, the weighted average height of the closure errors of all triangles are considered. By iterative calculation, the atmospheric refraction coefficient is estimated. The estimated atmospheric refraction coefficient is applied successfully to the adjustment and analysis of the external control network of a dam. In this aspect, one does not need to solve the atmospheric refraction coefficient for each station and edge. Moreover, one of the most suitable atmospheric refraction coefficients for the whole trigonometric leveling network can be provided from the relevant conditions. The approach provides the advantages of flexibility and practicality, effectively weakening the influence of the atmospheric refraction coefficient on measurement accuracy in the trigonometric leveling network. Furthermore, it provides a theoretical basis for further engineering applications.

Time-series SAR interferometry (TS-InSAR) has been widely used to map the millimeter-scale interseismic displacements. Tropospheric noise is still a key error source that hinders further improvement of such measurement. In this article, a new spatiotemporal TS-InSAR tropospheric noise filtering is proposed to approach higher accuracy measurements. We first construct a 2-D arc network for all the data points, and based on that a temporal high-pass and spatial low-pass filtering is applied to estimate the tropospheric noise for all the points. To better filter out long-wavelength interseismic displacements in temporal high-pass filtering, we construct four-candidate time-series models to model the displacement histories for each arc. To avoid overfitting, the F hypothesis test is applied to select the most suitable model for all arcs. Notably, instead of the commonly used Delaunay network, the all-pairs-shortest-path algorithm in the graph theory is employed to reconstruct all arcs and to improve the applicability of the time-series model. Comprehensive tests using synthetic data and Sentinel-1 data covering Central San Andreas Fault (CSAF) creep section validate our tropospheric noise removal approach in measuring the interseismic velocity across the fault.

The third Nanjing Yangtze River Bridge is a steel pylon cable stayed bridge. Such a structure is new in China and therefore brings new challenges to the work of control surveys. This paper describes the survey methods used for building and erecting the steel pylons and for construction of the steel tower. The survey methods adopted have proven successful and can be used as reference for similar projects in the future.