Robust GNSS positioning in urban environments is still plagued by multipath
effects, particularly due to the complex signal propagation induced by
ubiquitous surfaces with varied radio frequency reflectivities. Current 3D
Mapping Aided (3DMA) GNSS techniques show great potentials in mitigating
multipath but face a critical trade-off between computational efficiency and
modeling accuracy. Most approaches often rely on offline outdated or
oversimplified 3D maps, while real-time LiDAR-based reconstruction boasts high
accuracy, it is problematic in low laser reflectivity conditions; camera 3DMA
is a good candidate to balance accuracy and efficiency but current methods
suffer from extremely low reconstruction speed, a far cry from real-time
multipath-mitigated navigation. This paper proposes an accelerated framework
incorporating camera multi-view stereo (MVS) reconstruction and ray tracing. By
hypothesizing on surface textures, an orthogonal visual feature fusion
framework is proposed, which robustly addresses both texture-rich and
texture-poor surfaces, lifting off the reflectivity challenges in visual
reconstruction. A polygonal surface modeling scheme is further integrated to
accurately delineate complex building boundaries, enhancing the reconstruction
granularity. To avoid excessively accurate reconstruction, reprojected point
cloud multi-plane fitting and two complexity control strategies are proposed,
thus improving upon multipath estimation speed. Experiments were conducted in
Lujiazui, Shanghai, a typical multipath-prone district. The results show that
the method achieves an average reconstruction accuracy of 2.4 meters in dense
urban environments featuring glass curtain wall structures, a traditionally
tough case for reconstruction, and achieves a ray-tracing-based multipath
correction rate of 30 image frames per second, 10 times faster than the
contemporary benchmarks.

Cet article explore les excursions dans le temps et leurs implications.

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