A bimodal approach for land vehicle localization

Abstract

In this paper, we present a novel idea to integrate a low cost inertial measurement unit (IMU) and Global Positioning System (GPS) for land vehicle localization. By taking advantage of positioning data calculated from an image based on photogrammetry and stereo-vision techniques, errors caused by a GPS outage for land vehicle localization were significantly reduced in the proposed bimodal approach. More specifically, positioning data from the photogrammetric approach are fed back into the Kalman filter to reduce and compensate for IMU errors and improve the performance. Experimental results are presented to show the robustness of the proposed method, which can be used to reduce positioning errors caused by a low cost IMU when a GPS signal is not available in urban areas.