Abstract
Current constellation of global navigation satellite system (GNSS) ensures signal availability even in severe observational conditions like urban canyon or under tree canopy. However, positioning in such environment remains a challenge because obstacles can block, reflect and diffract GNSS signals which significantly affects accuracy. Those errors are strongly sight dependent and cannot be mitigated in differential positioning that is why, knowledge of the shape and spatial distribution of terrain obstacles is essential. In this paper using of airborne laser scanning (ALS) data for terrain obstacles inventory is presented and evaluated. In proposed method terrain obstacle models have been derived from ASCII ALS data file using open source QGIS with LAStools software suite and dedicated ALSObstModel plugin. Test models were developed for three geodetic control points with different environmental characteristics. For each point reference model from direct tachometry measurements have been obtained. An average error in determining the elevation of the terrain obstacles from ALS based models was 0.6° to 1.7°. This distance corresponds to 3 to 6 minutes of satellite in orbit.
Highlights
Wide availability of networks of permanent reference stations and current constellation of global navigation satellite system (GNSS), consists of almost 100 satellites make satellite positioning techniques very popular and readily used
Due to the fact that these errors are strongly sight dependent and cannot be mitigated in differential positioning, knowledge of the shape and spatial distribution of terrain obstacles is essential in proper mission planning as well as GNSS data processing
The paper presents a method of terrain obstacle modelling based on LIDAR data and analysis of the obtained models
Summary
Wide availability of networks of permanent reference stations and current constellation of global navigation satellite system (GNSS), consists of almost 100 satellites make satellite positioning techniques very popular and readily used. Over the last 20 years, many methods of generating obstacles diagrams have been proposed These methods can be divided into two categories: first requiring some observations directly at the measuring point, and second one based on spatial data sets. In the case of measurement campaigns taking place in large areas, performing direct measurements would consume too much time and resources In such cases, one should look for methods that allows to obtain the appropriate and accurate terrain obstacle models without having to perform a direct measurements. In addition the main limitation of those methods is the fact, that there are no complete 3D surface models (models including, apart from buildings, trees and other obstacles) for many areas, except large urban agglomerations. From the point of view of terrain obstacle modelling, ALS has a number of advantages, especially provides high dense 3D points cloud in which points represents land surface as well as every objects above it
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