Urban Localization Method for Mobile Robots Based on Dead Reckoning Sensors, GPS, and Map Matching

This paper presents a localization method in urban environments by using dead reckoning sensors, Global Positioning System (GPS), and taking into account the benefits of map matching. Extended Kalman Filter (EKF) is used as the main framework to fuse the information from sensors. However, the result of the EKF greatly depends on how the robot utilizes and judges the position measurement which comes from GPS since the GPS easily gives wrong position measurement due to the phenomenon called multipath effect. Under the assumption that the robot must operate only on the main road, a map matching is used to filter out the wrong GPS measurements which fall outside the main road. An experiment has been conducted in urban environment to validate the proposed method. Experimental results show that our proposed method has superior performance compared to the EKF without map matching