Intelligent Vehicle Absolute Localisation Method Using GIS Information: A Data Fusion Approach
Abstract
This paper describes a method that provides an estimated location of an outdoor vehicle relative to a digital road map using Belief Theory and Kalman filtering. Firstly, an Extended Kalman Filter combines the DGPS and odometer measurements to produce an approximation of the vehicle’s pose, which is then used to select the most likely segment from a road network database. The selection strategy merges several criteria based on distance, direction and velocity measurements using Belief Theory and a dedicated fusion operator. Thanks to this methodology, a Localization Uncertainty Gauge can be computed. This gauge indicates the level of confidence assigned to the selected road by the system. Real experimental results illustrate this approach.
