Volume 4, Issue 3

This paper describes an adaptive extended Kalman filter (AEKF) approach for geo-referencing tasks for a multi-sensor system (MSS). The MSS is a sensor fusion of a phase-measuring terrestrial laser scanner (TLS) with navigation sensors such as Global Navigation Satellite System (GNSS) equipment and inclinometers. The position and orientation of the MSS are the main parameters which are constant on a station and will be derived by a Kalman filtering process. Hence, the orientation of a TLS/MSS can be done without any demand for other artificial targets in the scanning area. However, using inclinometer measurements the spatial rotation angles about the X- and Y-axis of the fixed MSS station can be estimated by the AEKF. This makes it possible to determine all six degrees of freedom of the transformation from a sensor-defined to a global coordinate system. The paper gives a detailed discussion of the strategy used for the direct geo-referencing. The AEKF for the transformation parameters estimation is presented with focus on the modelling of the MSS motion. The usefulness of the suggested approach will be demonstrated using practical investigations.

Landslides are natural geomorphological phenomenas that can cause hazardous situations to men and infrastructure especially in densely populated areas. For the investigation of such events often numerical slope models are used, that describe the mechanical and geological properties of bedrock. However, the adaptation of the model to the monitored data is commonly done by ‘trial and error’ methods. In order to improve the adaptation process, adaptive Kalman-filtering techniques shall be used in terms of a realistic model calibration. Nevertheless, this method is very computationally intensive applied on full slope models with a larger grid size. Since the deformation process is normally restricted to limited parts of the investigation area (e.g. areas close to the surface), the Kalman-filter algorithm may be applied to selected parts of the grid with predicted major displacements. The first part of the paper is focussed on the monitoring design of the landslide ‘Steinlehnen’ (Tyrol, Austria). For this mass movement, a numerical model is currently under development and shall be calibrated by adaptive Kalman-filtering. In the second part, some investigation results for the adaptive Kalman-filtering approach are presented and discussed regarding a still simulated numerical test slope.

GNSS has become one of the most wide-spread measurement technologies, allowing cm-level positioning accuracy using RTK or Network RTK. Unfortunately, the system's major drawbacks are the requirement for a clear view of the sky and accuracy dependent on the geometric distribution of the satellites, not only varying throughout the day but also prone to location specific problems. With wide-spread utilisation of GNSS for monitoring of manmade structures and other civil engineering tasks, such shortcomings can be critical. One of possible solution is the deployment of a supporting system, such as Locata – a terrestrial positioning technology, which mitigates the need for a clear view of the sky and provides system integrity control. This paper, part of the proposed integration feasibility study, presents Locata performance indoors, its capacity and mitigation methods.

Geodesics on the surface of an ellipsoid of revolution may be represented by canonical equations. Applying known theorems, an integral of this equation system can be found. Its exact solution is given by a definite integral (quadrature). Using power series, this integral can be calculated with arbitrary precision. As an example, a fourth order solution will be presented, especially for geodetic applications.

The Shuttle Radar Topography Mission (SRTM) was revolutionary because it generated global digital elevation model (DEM) using space-borne radar imaging technology. The SRTM mapped almost 80 percent of the Earth's surface to a 1-arc-second (~30 m) resolution, but generated a lower resolution 3-arc-second (~90 m) DEM. Currently, the low resolution SRTM DEM is updated and interpolated using other higher resolution DEMs obtained from different sources or techniques, such as InSAR, photogrammetry, and airborne laser scanning. Of the many DEM techniques, the InSAR DEM generation technique is recommended due to the large-coverage and the fact that it is a similar technique to that used for the SRTM DEM. This paper describes a method for updating the SRTM DEM, using InSAR DEM based on the coherence value selection method.