Abstract
3D-mapping-aided (3DMA) global navigation satellite system (GNSS) positioning that improves positioning performance in dense urban areas has been under development in recent years, but it still faces many challenges. This paper details a new algorithm that explores the potential of using building boundaries for positioning and heading estimation. Rather than applying complex simulations to analyze and correct signal reflections by buildings, the approach utilizes a convolutional neural network to differentiate between the sky and building in a sky-pointing fisheye image. A new skymask matching algorithm is then proposed to match the segmented fisheye images with skymasks generated from a 3D building model. Each matched skymask holds a latitude, longitude coordinate and heading angle to determine the precise location of the fisheye image. The results are then compared with the smartphone GNSS and advanced 3DMA GNSS positioning methods. The proposed method provides degree-level heading accuracy, and improved positioning accuracy similar to other advanced 3DMA GNSS positioning methods in a rich urban environment.
Highlights
Global navigation satellite systems (GNSS) provide geographical longitude and latitude positioning with meter-level accuracy in open areas [1]
III DSLR) with the fisheye-lens (8–15 mm f/4L EF fisheye USM lens). Both the lens and DSLR were designed and manufactured by Canon Inc., a company based in Ōta, Tokyo, Japan
Skymask matching heading error and smartphone heading error for each image relative to the true north is shown in Table 5
Summary
Global navigation satellite systems (GNSS) provide geographical longitude and latitude positioning with meter-level accuracy in open areas [1]. This accuracy, suffers in dense urban areas because buildings block, reflect, and diffract the signals. These cause errors in satellite positioning and reduces accuracy and, in severe cases, the position error could exceed 50 m [2,3]. An improvement in the real time-positioning accuracy of low-cost GNSS systems in dense urban areas to within 5 m would benefit many different potential applications [4], such as cloud-sourced mobile mapping and object tracking. To improve the positioning performance in urban environments, researchers have designed different methods to identify correct or exclude the unhealthy measurements by receiver based
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