An efficient outdoor localization method using multi-sensor fusion for car-like robots

Abstract

An efficient outdoor local localization method is suggested using multi-sensor fusion with MU-EKF (Multi-UpdateExtended Kalman Filter) for car-like mobile robots. In outdoor environments, where mobile robots are used for explorations ormilitary services, accurate localization with multiple sensors is indispensable. In this paper, multi-sensor fusion outdoor locallocalization algorithm is proposed, which fuses sensor data from LRF (Laser Range Finder), Encoder, and GPS. First, encoder data isused for the prediction stage of MU-EKF. Then the LRF data obtained by scanning the environment is used to extract objects, andestimates the robot position and orientation by mapping with map objects, as the first update stage of MU-EKF. This estimation isfinally fused with GPS as the second update stage of MU-EKF. This MU-EKF algorithm can also fuse more than three sensor dataefficiently even with different sensor data sampling periods, and ensures high accuracy in localization. The validity of the proposedalgorithm is revealed via experiments. © ICROS 2011.