Mobile Robot Localization using GPS, IMU and Visual Odometry

In this work we present the localization and navigation for a mobile robot in the outdoor environment. It is based on fusing the data from IMU, differential GPS and visual odometry using the extended Kalman filter framework. First, the IMU provides the heading angle information from the magnetometer and angular velocity, and GPS provides the absolute position information of the mobile robot. The image-based visual odometry is adopted to derive the moving distance and additional localization information. Finally, the mobile robot position is further refined using the extended Kalman filter. The experiments are carried out in the outdoor environment. We compare the results with the original GPS raw data, and the performance of the presented method is evaluated.