Volume 8, Issue 3

The estimates based on laser scans of surfaces with unknown deformations are biased and not reproducible when changing the scanning geometry. While the existence of a bias is only disadvantageous at some applications, non-reproducible estimates are never desired. Hence, this varying bias and its origin need to be investigated - since this situation has not been examined sufficiently in the literature. Analyzing this situation, the dependence of the estimation on the network configuration is highlighted: the network configuration - studied similarly to geodetic networks - rules about the impact of the deformation. As pointed out, this impact can be altered by manipulating the network configuration. Therefore, several strategies are proposed. These include manipulations of the leastsquares adjustment as well as robust estimation. It is revealed that the reproducibility of the estimates can indeed be significantly increased by some of the proposed leastsquares manipulations. However, the bias can only be significantly reduced by robust estimation.

Best invariant quadratic unbiased estimates (BIQUE) of the variance and covariance components for a nonlinear Gauss Helmert (GH) model are derived. To detect outliers, the expectation maximization (EM) algorithm based on the variance-inflation model and the mean-shift model is applied, which results in an iterative reweighting least squares. Each step of the iterations for the EM algorithm therefore includes first the iterations for linearizing the GH model and then the iterations for estimating the variance components. The method is applied to fit a surface in three-dimensional space to the three coordinates of points measured, for instance, by a laser scanner. The surface is represented by a polynomial of second degree and the variance components of the three coordinates are estimated. Outliers are detected by the EM algorithm based on the variance-inflation model and identified by the EM algorithm for the mean-shift model.

The Australian Height Datum (AHD) is the current national vertical datum for Australia, and AUSGeoid09 is the latest quasigeoid model used to compute (normal-orthometric)AHDheights fromGlobalNavigation Satellite System (GNSS) derived ellipsoidal heights. While previous studies have evaluated the AUSGeoid09 model across Australia, such studies have not focused on mountainous regions in particular. This paper investigates the performance of AUSGeoid09 in an absolute sense in the Mid Hunter and Snowy Mountains regions of New South Wales. Absolute (i.e. single point) comparisons were undertaken between AUSGeoid09-derived heights and published AHD heights. The performance of AUSGeoid09 was evaluated relative to its predecessor AUSGeoid98. In both study areas, an overall improvement is evident when applying AUSGeoid09 to compute AHD heights in an absolute sense. In the MidHunter, AUSGeoid09 provided a substantial improvement over its predecessor, clearly demonstrating the benefits of its new geometric component on GNSS-derived AHD height determination. In the Snowy Mountains, moderate improvement over AUSGeoid98 was evident. However, a slope was detected for AUSGeoid09 residuals, and it appears that the geometric component may have overcompensated for sea surface topography in this area. While this appraisal of AUSGeoid09 performance in mountainous regions is encouraging, it has been shown that some discrepancies still remain between AUSGeoid09-derived heights and AHD. Eventually, a new vertical datum will be necessary to ensure homogeneity across Australia.

The Precise Point Positioning (PPP) GPS data processing technique has developed over the past 15 years to become a standard method for growing categories of positioning and navigation applications. The technique relies on single receiver point positioning combined with precise satellite orbit and clock information, pseudorange and carrier-phase observable filtering, and additional error modelling. Uniquely addressed is the current accuracy of the technique, and explains the limits of performance, which will be used to define paths for future improvements of the technology. PPP processing of over 300 International GNSS Service (IGS) stations over one week results in few millimetre positioning rms error in the north and east components and centimetre-level in the vertical (all one sigma values). These results are categorised into quality classes in order to analyse the root causes of the resultant errors: "best", "worst", multipath, antenna displacement effects, satellite availability and geometry, etc. Also of interest in PPP performance is solution convergence period. Static, conventional solutions are slow to converge, with approximately 20 minutes required for 95% of solutions to reach a horizontal accuracy of 20 cm or better. From the above analysis, the limitations of PPP and the source of these limitations are isolated, including site displacement modelling, geometric measurement strength, pseudorange multipath and noise, etc. It is argued that new ambiguity resolution and multi-GNSS PPP processing will only partially address these limitations. Improved modelling is required for: site displacement effects, pseudorange noise and multipath, and pseudorange and carrier-phase biases. As well, more robust undifferenced carrier phase ambiguity validation and improved stochastic modelling is required for the pseudorange and carrier-phase observables to allow for more realistic position uncertainties.

Image Assisted Total Stations (IATS) unify geodetic precision of total stations with areal coverage of images. The concept of using two IATS devices for high-resolution, long-range stereo survey of georisk areas has been investigated in the EU-FP7 project DE-MONTES (www.de-montes.eu). The paper presents the used methodology and compares the main features with other terrestrial geodetic geo-monitoring methods. The theoretically achievable accuracy of the measurement systemis derived and verified by ground truth data of a distant clay pit slope and simulated deformations. It is shown that the stereo IATS concept is able to obtain higher precision in the determination of 3D deformations than other systems of comparable sensor establishment effort.