The lack of marine gravimetric measurements and the presence of severely contaminated altimetry data pose multiple challenges in high-quality quasi-geoid (QG) and mean dynamic topography (MDT) determination over islands, where the application of nadir altimetry alone is inadequate. We explore the potential for regional enhancement using wide-swath data from the Surface Water and Ocean Topography (SWOT) mission. Numerical experiments over the Paracel Islands in South China Sea underscore the superiority of using SWOT data in QG/MDT computation. Comparisons with the airborne gravimetry-derived QG reveal that the Root Mean Square Errors (RMSEs) of QGs derived from the SWOT data are reduced by 38.24–60.90% compared to those derived solely from nadir altimetry. The QG profiles retrieved from the Sentinel-3A/B altimetry using the fully-focused SAR technology are effective in discriminating the quality of different QGs near islands. The RMSEs of SWOT-derived QGs constitute reductions of 6.27–17.52% compared to those computed from nadir altimetry alone. Notable improvements up to 4 cm are observed when Sentinel-3A/B tracks approached islands. The mutual comparison of the QGs computed from the SWOT gravity anomaly (GRA) and vertical gravity gradient (VGG) data suggests that the VGG-derived QG has improved quality, and the utilization of VGG recovers more small-scale signals. The SWOT-derived MDTs reduce the bubble-like errors up to several centimeters compared to those computed exclusively using nadir altimetry. Our findings highlight that utilizing SWOT data enables the acquisition of an accurate QG/MDT with an RMSE less than 1 cm compared to that derived from airborne gravimetry.

Variance component estimation (VCE) is widely applied to adjust random models in the fusion processing of multiple classes of observations. In our previous study, the least-squares VCE (LS-VCE) for the classical Gauss–Markov (GM) model was extended to a universal adjustment model: the nonlinear Gauss–Helmert (GH) model. However, due to its limited ability to resist outliers, the accuracy of the estimated variance components and parameters will be negatively affected in the presence of outliers. In this article, the variance inflation principle of robust estimation is further introduced based on our previous study, and a robust LS-VCE method for the nonlinear GH model is proposed. To avoid the emergence of negative variance components, the nonnegative estimation of the variance components is achieved as well. Unlike the existing studies, the new method can simultaneously mitigate the negative influence of outliers while reasonably adjusting the relative weighting ratios among different classes of observations in the nonlinear GH model. Finally, case studies of point cloud fitting based on original observations and geodetic symmetric transformation are carried out to validate the performance of the proposed method. The results show that when the observations are polluted by outliers, the accuracy of the parameters obtained by the new method has considerable improvement compared with that from the generalized total least-squares and the LS-VCE method for the nonlinear GH model. Since the linear/nonlinear GM and errors-in-variables (EIV) models can be treated as special cases of the nonlinear GH model, the proposed method possesses a wide range of applicability.

Monocular vision sensors are often affected by the rapid direction change in load platform and violent illumination change when the mobile device moves autonomously with high maneuverability. The images collected by the visual sensor will also have a lot of dynamic blur, which together with the weak texture environment reduces the continuity and accuracy of the visual autonomous navigation system. To enhance the stability of the system, in the letter, we propose a vision-led multisource data fusion navigation algorithm. The system combines the visual information for trajectory estimation, adds the inertial measurement unit (IMU) measurement information to the sliding window for optimization, and finally uses the global navigation satellite system (GNSS) data as a reconstraint condition through factor graph optimization to further optimize the trajectory accuracy. Experiments on the public datasets containing a variety of different scene categories show that the trajectory tracking results generated by our algorithm are more complete and stable and can better meet the system’s autonomous navigation requirements.

The new generation of SAR satellites is serving our long-standing demand for high-resolution crustal deformation over various scales. However, the reliability of InSAR measurements is still limited by varying tropospheric conditions between acquisitions, especially when mapping slow-deforming interseismic deformation. We propose here a new phase-based approach for mapping interseismic deformation using short-period interferograms. Our method formulates the InSAR phase after topographic correction as the sum of three components: (1) spatiotemporally varied turbulent tropospheric phase, (2) topography-correlated stratified tropospheric phase, and (3) interseismic-related deformation assumed to be accumulated at a constant rate. We simultaneously solve for the parameters in the model to avoid overestimating the tropospheric phases, especially when interseismic deformation and tropospheric delays are both coupled with elevation in space. Synthetic tests and practical applications to easternmost Altyn Tagh fault demonstrate that the new method can effectively recover the small-amplitude interseismic deformation caused by fault motion even when the interferograms are dominated by strong tropospheric delays.

Recently, the 3D symmetric transformation, which can simultaneously consider the measurement errors of the source and target coordinates, has become a hot research topic especially in geodetic datum transformation and point clouds registration. However, there are rare studies on outlier resisting for 3D symmetric transformation and most of them only focused on similarity transformation. In this study, to unify affine, similarity, and rigid transformations, a universal expression of 3D symmetric transformation was abstracted as a partial EIV model with equality constraints. Then the equality constrained partial EIV model was solved by a proposed constrained total least squares (CTLS) algorithm and the corresponding efficient formulae were derived. Finally, a robust CTLS (RCTLS) algorithm of universal 3D symmetric transformation was presented by introducing the equivalent weight principle of robust estimation. Unlike the existing algorithms, the RCTLS can efficiently deal with all kinds of 3D symmetric transformation problems influenced by outliers with a unified formula framework. To verify the performance of the RCTLS, comparative experiments were employed in two simulated scenarios of similarity and affine transformation and a real-world rigid transformation scenario for point clouds registration, respectively. The proposed RCTLS was compared with three non-symmetric transformation algorithms (dual quaternion-based algorithm, linear least squares and singular value decomposition) and two symmetric transformation algorithms (outlier-detected TLS (OD-TLS) and CTLS) in terms of accuracy, robustness and computational efficiency. The results indicated both OD-TLS and RCTLS are robust to outliers with higher transformation accuracy, but the proposed RCTLS is more universal and efficient than OD-TLS.

The registration of 3-D point clouds is an important procedure during the terrestrial laser scanning data processing. Recently, due to their high flexibility and the powerful mathematical model, a large amount of least-squares-based (LSs-based) methods are proposed and widely applied to estimate the transformation parameters of 3-D point clouds registration. In these LSs-based methods some based on the generalized Gauss–Markov model do not correct the influence of random errors on source 3-D point clouds. Although there are other methods based on the errors-in-variables (EIV) model, they are inapplicable for transformation problems with large rotation angles and arbitrary scale ratio. In addition, the gross errors are usually ignored in previous studies on 3-D point clouds registration, which, however, exists commonly and could distort the registration severely. Aiming to avoid the influence of gross errors and extend its application, an advanced outlier detected total least-squares (OD-TLS) method is proposed in this paper. Based on the generalized EIV model OD-TLS performs a seven-parameter 3-D similarity transformation with large rotation angles and arbitrary scale ratio. The random errors of both source and target 3-D point clouds are considered. Furthermore, outliers are detected and removed automatically by combining the data snooping method with total least-squares (TLS) estimation. In order to indicate the benefits of OD-TLS, comparative experiments with the LS3D and weighted total least squares (WTLS) on synthetic and real-world scanned 3-D point clouds were performed. The experimental results show OD-TLS not only enhances the registration accuracy but also increases its robustness.