To provide an accurate positioning, the land vehicle navigation applications are based on global positioning system (GPS). The addition of a digital road map allows locating the vehicle continuously and helps the driver to get the best path. These systems are usually enhanced with dead reckoning sensors due to GPS outages in urban areas in particular. For instance, the odometer sensors can be used to correct the vehicle location in this case. We present here a global estimation method of solving the fusion problem of the GPS, odometer, and digital road map measurements in the presence of GPS outages. It relies on a hybrid filter that takes advantage of the combination of a Kalman filter, which computes the linear part of the state equations and a particle filter to provide an optimal resolution scheme. When GPS fails, the filter fuses all available pseudorange measures to improve the vehicle positioning. In the case of an urban transport scenario, the results show that the number of particles is significantly reduced to achieve the same performance of a single particle filter in terms of accuracy. Moreover, software solutions can be developed for real-time applications.

This paper highlights a method to detect topological changes of digital road maps automatically. Here, the road network updating can be done in real time. It relies on collected GPS data from vehicles. These data are fused with existing road map data by an unscented Kalman filter in a centralized scheme. We modeled the map data as a sensor that allows accounting for the errors and uncertainties of land surveying. The core of our method is a probabilistic map-matching approach that is used to manage the road network database through the computation of the Mahalanobis distance. We show experimental results from an urban transport network scenario in which we deal with the case of opened roads and the recent conversion of a crossroad into a roundabout. Then, the obsolete map database is updated to ensure a more reliable route planning.

A growing number of applications needs GIS mapping information and commercial 3-D roadmaps especially. This paper presents a solution of accessing freely to 3-D map information and updating in the context of transport applications. The method relies on the OSM road networks that is 2-D modeled intrinsically. The objective is to estimate the road elevation and inclination parameters by fusing GPS, OSM and DEM data through a nonlinear filter. An experimental framework, using ASTER GDEM2 data, shows some results of the improvement of the roads modeling that includes their slopes also. The map database can be enriched with the estimated inclinations. The accuracy depends on the GPS and DEM elevation errors (typically a few meters with the GNSS sensors used and the DEM under consideration).

We present in this paper a method for land-vehicle navigation systems which fuses GPS and dead reckoning data especially in urban areas where GPS outages can happen. The DR sensor here computes the differential odometry of the vehicle. In order to improve the positioning accuracy, our algorithm also fuses the road map database of a GIS. The digital road map here is modeled as a measure described by a set of node locations and road bearings. This measurement equation allows a direct modeling of the map errors and uncertainties that can be embedded in the filter, and it is initialized by a matching procedure that uses the Mahalanobis metric as a statistical distance criterion. Based on a centralized fusion scheme, the filter sequentially processes the available measurements. We present experimental results from an urban transport network scenario in presence of GPS outages, which shows the benefits of the proposed method for the vehicle positioning.