Abstract
The positioning accuracy with good GNSS observation can easily reach centimetre level, supported by advanced GNSS technologies. However, it is still a challenge to offer a robust GNSS based positioning solution in a GNSS degraded area. The concept of GNSS shadow matching has been proposed to enhance the GNSS based position accuracy in city canyons, where the nearby high buildings block parts of the GNSS radio frequency (RF) signals. However, the results rely on the accuracy of the utilized ready-made 3D city model. In this paper, we investigate a solution to generate a GNSS shadow mask with mobile laser scanning (MLS) cloud data. The solution includes removal of noise points, determining the object which only attenuated the RF signal and extraction of the highest obstruction point, and eventually angle calculation for the GNSS shadow mask generation. By analysing the data with the proposed methodology, it is concluded that the MLS point cloud data can be used to extract the GNSS shadow mask after several steps of processing to filter out the hanging objects and the plantings without generating the accurate 3D model, which depicts the boundary of GNSS signal coverage more precisely in city canyon environments compared to traditional 3D models.
Highlights
GNSS plays an important role in current navigation applications in a pervasive way
mobile laser scanning (MLS) data used for the experiment were collected by ROAMER mobile mapping system developed by the Finnish Geospatial Research Institute (FGI) as Figure 8 presents, in Tapiola shopping centre area, Espoo, Finland, which is a 300by-300-meter area with buildings of varying size and height [39]
By analysing the MLS point cloud data and by applying developed adaptive spatial filter (ASF), it is concluded that MLS data can be used to extract GNSS shadow masks after a series of appropriate processing steps to filter out the hanging objects and the plantings
Summary
GNSS plays an important role in current navigation applications in a pervasive way. the accuracy and availability of such solutions in city canyons are a well-known problem, which has attracted researchers’ attention in the last few decades. A corresponding GNSS shadow match technique has been evaluated and proved its capability of refining the positioning accuracy by many research groups recently [1,2,3,4,5,6,7,8,9,10,11,12,13], especially, researchers from Britain: they proposed the idea and simulated the urban canyon case with multiple GNSS constellations scenario [3] and utilized the 3D city model of London to verify the idea [4, 5] with visibility scoring algorithm [4] to achieve optimized positioning results in London Such technology was to be investigated in Finland [8], United States [9,10,11], Canada [12], and Taiwan [13].
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