Hybrid Filtering Framework Based Robust Localization for Industrial Vehicles

Abstract

This paper presents a precise and robust localization framework for autonomous vehicles. In contrast to simultaneous localization and mapping, localization and mapping in our method are separate (i.e., first mapping and then localization within this map). The map used in this paper is a 3-D occupancy map that is generated from a stitched point cloud. For localization, a hybrid filtering framework is proposed to match the live data with the prior map. In the upper layer, odometer data, IMU data, and the map matching result are fused by a cubature Kalman filter, which will limit the predictive pose within a reasonable bound. In the lower layer, the map matching problem is converted into a point set registration problem that is solved using a particle filter, which will make the matching result robust to local minima. This hybrid scheme makes localization more robust to convergence to a local minimum, which is often encountered in the localization task for autonomous vehicles. This method can also guarantee decimeter-level precision in industrial environments. Experiments demonstrate the validity of this method and also show that it outperforms some state-of-the-art methods.