Fusion of GPS/OSM/DEM Data by Particle Filtering for Vehicle Attitude Estimation

The objective of this work is to estimate the localization and attitude of a land-vehicle by fusing GPS, OSM and DEM data through a nonlinear filter. We focus on the heading and pitch angles of the vehicle, knowing that these parameters are essential in the optimization of the route planning and energy management for an EV. This paper investigates the performance of particle filtering and probabilistic map-matching algorithms for tracking a vehicle with the help of digital roadmaps to improve the ground-location. Also, the filter fuses DEM data through a TIN method in order to bound altitude errors caused by GPS. The proposed method is evaluated through an urban transport network scenario and experimental results show that the proposed estimator can accurately estimate the vehicle location and attitude.