Accurate Localization in Urban Environments Using Fault Detection of GPS and Multi-sensor Fusion

In order to make robots perform tasks autonomously, it is necessary for robots to know the surrounding environments. Therefore, a world modeling should be made in advance or concurrently. It is important to know an accurate position for the accurate world modeling. The aim of this paper is an accurate localization method for the world modeling under the situation where the portion of signals from global positioning system (GPS) satellites is blocked in urban environments. In this paper, we propose a detection method for non-line-of-sight satellites and a localization method using the GPS, the inertial measurement unit (IMU), the wheel encoder, and the laser range finder (LRF). To decide whether the signal from the satellite is blocked by the building, the local map that is made from the local sensors and an LRF is exploited. Then the GPS reliability is established adaptively in a non-line-of-sight situation. Through an extended Kalman filter (EKF) with the GPS reliability the final robot pose is estimated. To evaluate the performance of the proposed methods, the accuracy of the proposed method is analyzed using ground truth from Google maps. Experimental results demonstrate that the proposed method is suitable for the urban environments.