Matching Raw GPS Measurements on a Navigable Map Without Computing a Global Position
Résumé
Map-matching means determining the location of a mobile with respect to a road network description stored in a digital map. This problem is usually addressed using GPS-like fixes. Unfortunately, there are many situations in urban areas where few satellites are visible because of outages due to tall buildings. In this paper, map-matching is solved using raw GPS measurements (pseudoranges and Doppler measurements), avoiding the necessity to compute a global position. The problem is formalized in a general Bayesian framework in order to handle noise, which is able to perform multi-hypothesis map-matching when there is not enough information to make unambiguous decisions. This tightly-coupled GPS-Map fusion has to cope simultaneously with identifying the road and estimating the mobile's position on that road. A marginalized particle filter is proposed for solving this hybrid estimation problem efficiently. Real experimental results are reported to show that this approach can be initialized with fewer than four satellites. It is also able to track the location with two satellites only, once the road selection has been solved.
Origine | Fichiers produits par l'(les) auteur(s) |
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