Convergence of daily mean coordinates of precise positioning methods

As part of the research of modern movements of the Earth’s crust, an analysis of 7 high-precision methods for calculating GNSS positions was carried out for the convergence of their daily mean coordinates. Based on Euclidean distances, regular and maximal discrepancies between coordinates of different methods are given. According to the coordinates in the ITRF, 5 methods are stood out with regular coordinate discrepancies <1 mm, and individual maximum discrepancies up to 30 mm. The other two methods have regular discrepancies in coordinates up to 2 cm, and the maximum differences reach 1 m. For a group of stations global coordinates transformation into a local reference frame leads to the effect of coordinate stabilization and increases their relative precision in the time series. As a result of such procedure, the level of maximum coordinate discrepancies between the methods decreased to 46%. Moreover, one of the methods of calculating coordinates has improved its convergence with the other methods by 80%. Based on the Euclidean distance method, the quality of the raw data for each station was evaluated. Thus, there is a group of 8 stations, for which the convergence of coordinates in different methods are approximately at the same level, and 2-3 times better than for the other 2 stations.

A Comparative Study of Weighting Methods for Local Reference Frame

In the field of photogrammetric engineering, computer vision, and graphics, local shape description is an active research area. A wide variety of local shape descriptors (LSDs) have been designed for different applications, such as shape retrieval, object recognition, and 3D registration. The local reference frame (LRF) is an important component of the LSD. Its repeatability and robustness directly influence the descriptiveness and robustness of the LSD. Several weighting methods have been proposed to improve the repeatability and robustness of the LRF. However, no comprehensive comparison has been implemented to evaluate their performance under different data modalities and nuisances. In this paper, we focus on the comparison of weighting methods by using six datasets with different data modalities and application contexts. We evaluate the repeatability of the LRF under different nuisances, including occlusion, clutter, partial overlap, varying support radii, Gaussian noise, shot noise, point density variation, and keypoint localization error. Through the experiments, the traits, advantages, and disadvantages of weighting methods are summarized.

Pointing Errors in Non-Metric Virtual Environments

There have been suggestions that human navigation may depend on representations that have no metric, Euclidean interpretation but that hypothesis remains contentious. An alternative is that observers build a consistent 3D representation of space. Using immersive virtual reality, we measured the ability of observers to point to targets in mazes that had zero, one or three ‘wormholes’ – regions where the maze changed in configuration (invisibly). In one model, we allowed the configuration of the maze to vary to best explain the pointing data; in a second model we also allowed the local reference frame to be rotated through 90, 180 or 270 degrees. The latter model outperformed the former in the wormhole conditions, inconsistent with a Euclidean cognitive map.

GPS-derived ground deformation (2005–2014) within the Gulf of Mexico region referred to a stable Gulf of Mexico reference frame

This study investigates current ground deformation derived from the GPS geodesy infrastructure in the Gulf of Mexico region. The positions and velocity vectors of 161 continuous GPS (CGPS) stations are presented with respect to a newly established local reference frame, the Stable Gulf of Mexico Reference Frame (SGOMRF). Thirteen long-term (> 5 years) CGPS are used to realize the local reference frame. The root mean square (RMS) of the velocities of the 13 SGOMRF reference stations achieves 0.2 mm yr−1 in the horizontal and 0.3 mm yr−1 in the vertical directions. GPS observations presented in this study indicate significant land subsidence in the coastal area of southeastern Louisiana, the greater Houston metropolitan area, and two cities in Mexico (Aguascalientes and Mexico City). The most rapid subsidence is recorded at the Mexico City International airport, which is up to 26.6 cm yr−1 (2008–2014). Significant spatial variation of subsidence rates is observed in both Mexico City and the Houston area. The overall subsidence rate in the Houston area is decreasing. The subsidence rate in southeastern Louisiana is relatively smaller (4.0–6.0 mm yr−1) but tends to be steady over time. This poses a potential threat to the safety of coastal infrastructure in the long-term.