Mosaic Zonotope Shadow Matching for Risk-Aware Autonomous Localization in Harsh Urban Environments. (arXiv:2205.10223v1 [cs.AI])

Risk-aware urban localization with the Global Navigation Satellite System
(GNSS) remains an unsolved problem with frequent misdetection of the user's
street or side of the street. Significant advances in 3D map-aided GNSS use
grid-based GNSS shadow matching alongside AI-driven line-of-sight (LOS)
classifiers and server-based processing to improve localization accuracy,
especially in the cross-street direction. Our prior work introduces a new
paradigm for shadow matching that proposes set-valued localization with
computationally efficient zonotope set representations. While existing
literature improved accuracy and …

ai arxiv autonomous localization risk shadow