Abstract-This paper presents a data fusion strategy for the global localization of car-like vehicles. The system uses raw GNSS measurements, dead-reckoning sensors and road map data. We present a new method to use the map information as a heading observation in a Kalman filter. Experimental results show the benefit of such a method when the GPS information is not available. Then, we propose a conservative localization strategy that relies mainly on dead-reckoned navigation. The GNSS measurements and the map information are not used when consistency tests are doubtful. Experimental tests indicate that the performance is effectively better when using only the available consistent information.

Abstract-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 GPSlike 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.

Global Navigation Satellite Systems (GNSS) are often used to localize a receiver with respect to a given map. This association problem, also known as map-matching, is usually addressed using estimated positions computed by the GNSS receiver. In this paper we propose a method that combines the cartographic data in the GNSS computation fix itself. We focus on the use of a road network provided by cartographers such as NavTeQ or TeleAtlas. Geo-referenced data is modelled by segments that can be used as constraints or fused with the pseudoranges. Using residuals, a new method for tackling the underlying problem of the road selection is proposed. We show that this approach is also well adapted to the integrity problem of map-matching, since a consistency test is derived. Experimental results illustrate the performance of this method with different maps.