Localization in GNSS-denied/challenged indoor/outdoor and transitional environments represents a challenging research problem. This paper reports about a sequence of extensive experiments, conducted at The Ohio State University (OSU) as part of the joint effort of the FIG/IAG WG on Multi-sensor Systems. Their overall aim is to assess the feasibility of achieving GNSS-like performance for ubiquitous positioning in terms of autonomous, global, preferably infrastructure-free positioning of portable platforms at affordable cost efficiency. In the data acquisition campaign, multiple sensor platforms, including vehicles, bicyclists and pedestrians were used whereby cooperative positioning (CP) is the major focus to achieve a joint navigation solution. The GPSVan of The Ohio State University was used as the main reference vehicle and for pedestrians, a specially designed helmet was developed. The employed/tested positioning techniques are based on using sensor data from GNSS, Ultra-wide Band (UWB), Wireless Fidelity (Wi-Fi), vison-based positioning with cameras and Light Detection and Ranging (LiDAR) as well as inertial sensors. The experimental and initial results include the preliminary data processing, UWB sensor calibration and Wi-Fi indoor positioning with room-level granularity and platform trajectory determination. The results demonstrate that CP techniques are extremely useful for positioning of platforms navigating in swarms or networks. A significant performance improvement in terms of positioning accuracy and reliability is achieved. Using UWB, decimeter-level positioning accuracy is achievable under typical conditions, such as normal walls, average complexity buildings, etc. Using Wi-Fi fingerprinting, success rates of approximately 97 % were obtained for correctly detecting the room-level location of the user.
Hellenic Military Geographical Service (HMGS) has established and measured various networks in Greece which constitute the geodetic infrastructure of the country. One of them is the triangulation network consisting of about 26.000 pillars all over Greece. Classical geodetic measurements that held by the Hellenic Military Geographic Service (HMGS) through the years have been used after adjustment for the state reference frame which materializes the current Hellenic Geodetic Reference System of 1987 (HGRS87). The aforementioned Reference System (RS) is a static one and is in use since 1990. Through the years especially in the era of satellite navigation systems many Global Navigation Satellite System (GNSS) networks have been established. The latest such network materialized by HMGS is ongoing and covers until now more than the 2/3 of the country. It is referenced by International GNSS Service (IGS) permanent stations and consists a local densification IGS08 Reference Frame. Firstly, this gives the opportunity to calculate transformation parameters between the two systems and a statistical analysis of the residuals leads to intermediate conclusions. After that and in conjunction with existing past transformations, tectonic deformations and their directions are concluded. Moreover past GPS observations on the same pillars in compare to the newer ones give also a sense of tectonic displacements. Greece is one of the most tectonically active countries in Europe and the adoption of a modern kinematic or semi-kinematic geodetic datum is a necessity as it should incorporate a deformation model like 3d velocities on the reference frame realization. The detection of geodynamic changes is a continuous need and should be taken into consideration at each epoch.
On the base of International Terrestrial Reference Frame 2008 (ITRF2008) a new global plate model of station positions and velocities with accuracy 1–3 mm and 1 mm per year respectively was established. Next, this model was used in our paper for plate motion parameters estimation for the major plates as Eurasian, North American, Pacific and small plates as Australian, African and Antarctic on the base of the observation campaigns for three techniques: Satellite Laser Ranging (SLR), Doppler Orbitography by Radiopositioning Integrated on Satellite system (DORIS) and Very Long Baseline Interferometry (VLBI), each technique was analyzed separately. Investigation for GNSS technique is scheduled to take place in the future. The plate motion parameters were adjusted using least squares method and sequential solution. In the first stage, the plate motion parameters were determined for two selected stations and next stations were added until stability of the solution was observed. Final results of our solution were compared with the APKIM 2005 IGN model by H. Drewes. Agreement of solutions is order 2 degrees or better.
In this work, the direct geodesic problem in Cartesian coordinates on a triaxial ellipsoid is solved by an approximate analytical method. The parametric coordinates are used and the parametric to Cartesian coordinates conversion and vice versa are presented. The geodesic equations on a triaxial ellipsoid in Cartesian coordinates are solved using a Taylor series expansion. The solution provides the Cartesian coordinates and the angle between the line of constant v and the geodesic at the end point. An extensive data set of geodesics, previously studied with a numerical method, is used in order to validate the presented analytical method in terms of stability, accuracy and execution time. We conclude that the presented method is suitable for a triaxial ellipsoid with small eccentricities and an accurate solution is obtained. At a similar accuracy level, this method is about thirty times faster than the corresponding numerical method. Finally, the presented method can also be applied in the degenerate case of an oblate spheroid, which is extensively used in geodesy.
Areal deformation monitoring based on point clouds can be a very valuable alternative to the established point-based monitoring techniques, especially for deformation monitoring of natural scenes. However, established deformation analysis approaches for point clouds do not necessarily expose the true 3D changes, because the correspondence between points is typically established naïvely. Recently, approaches to establish the correspondences in the feature space by using local feature descriptors that analyze the geometric peculiarities in the neighborhood of the interest points were proposed. However, the resulting correspondences are noisy and contain a large number of outliers. This impairs the direct applicability of these approaches for deformation monitoring. In this work, we propose Feature to Feature Supervoxel-based Spatial Smoothing (F2S3), a new deformation analysis method for point cloud data. In F2S3 we extend the recently proposed feature-based algorithms with a neural network based outlier detection, capable of classifying the putative pointwise correspondences into inliers and outliers based on the local context extracted from the supervoxels. We demonstrate the proposed method on two data sets, including a real case data set of a landslide located in the Swiss Alps. We show that while the traditional approaches, in this case, greatly underestimate the magnitude of the displacements, our method can correctly estimate the true 3D displacement vectors.
A positioning approach combining satellite measurements with a map representing the ground-truth trajectory is developed with the main objective of improving the availability of solutions for a mobile vehicle. For the positioning model, the Precise Point Positioning (PPP) technique is augmented with an alternative map-matching to find a probable space where the true vehicle or platform position is located. Then, by using a selection criterion based on the precise carrier phase residuals, the best candidate position within the space can be determined. This process provides an accurate initial position to the PPP filter, different from the standard PPP approach that relies on a point position using the less accurate pseudorange observables. A controlled experiment of a mobile receiver navigating over a pre-defined trajectory was conducted. The results show that the approach offers an instantaneous initial convergence, eliminating the re-convergences during two GNSS obstructions of 32 and 17 seconds, while constantly keeping the solution on the correct trajectory, even when tracking 3 to 2 satellites. This approach outperforms the standard PPP and RTK solutions in terms of convergences and re-convergences. These results are corroborated when comparing the average and standard deviation of residuals to the standard PPP model. For the pseudorange residuals, improvements of 17.5 cm and 24.3 cm in the average and standard deviation respectively were achieved. The carrier phase residuals standard deviation of the proposed approach was 3 cm better than that of the standard PPP.
The paper presents a closed-form solution to the point-wise weighted similarity transformation and its variants in the least squares framework under two estimation scenarios.
In the first scenario a target system is subject to random errors whilst in the second one a source system is considered to be erroneous. These transformation models will be named asymmetric in contrast to the symmetrical one solved under the errors-in-variables model where both systems are contaminated by random errors. The entire derivation is based on Procrustes Analysis. The formulas presented herein hold for both 2D and 3D transformations without any modification. The solution uses a polar decomposition to recover the rotation matrix.
This paper concerns two types of Msplit estimation: squared Msplit estimation (SMS), which assumes normality of observation errors and absolute Msplit estimation (AMS), which applies norm criterion. The main objective of the paper is to assess the accuracy of such estimators in vertical displacement analysis by applying Monte Carlo simulations. Another issue is to compare the accuracy of both estimators with the accuracy of the least squares estimation (LS). The paper shows that the accuracy of both Msplit estimates is like the accuracy of LS estimates. However, if some nonrandom errors occur, then accuracy of AMS estimates might be better than the accuracy of the rest of the estimates considered here. It stems from the fact that AMS estimates are robust against disturbances which have a small magnitude. It is also worth noting that the accuracy of both Msplit estimates might depend on the magnitude of the displacement.