Autonomous vehicle localization method based on an extended Kalman filter and geo-referenced landmarks

Abstract

Autonomous vehicles are considered a viable technological option to implement first/last mile transportation in the cities of tomorrow with a high population density, and for this reason it is essential that they have a robust localization system for the routes first-mile transport and last-mile transport points, and the route’s planning and navigation. This article presents the implementation of an outdoor parking localization system which uses a map based on geo-referenced landmarks (road marking poles with reflective tape) and an Extended Kalman Filter, fed with both odometry and 3D LiDAR information. The system was evaluated in nine routes with distances between 85 m and 360 m, in which an error was obtained between the ground-truth and the algorithm’s estimated position below 0.3 m and 0.5 m for the position in X and Y coordinates, respectively. The results show that this is a promising method that should be tested in larger settings using both natural and artificial landmarks.