"Circumventing Dynamic Modeling: Evaluation of the ErrorState Kalman Filter applied to Mobile Robot Localization"
The mobile robot localization problem is treated as a twostage iterative estimation process. The attitude is estimated first and is then available for position esti mation. The indirect (error state) form of the Kalman filter is developed for attitude estimation when apply ing gyro modeling. The main benefit of this choice is that complex dynamic modeling of the mobile robot and its interaction with the environment is avoided. The filter optimally combines the attitude rate information from the gyro and the absolute orientation measure ments. The proposed implementation is independent of the structure of the vehicle or the morphology of the ground. The method can easily be transfered to another mobile platform provided it carries an equivalent set of sensors. The 2D case is studied in detail first. Results of extending the approach to the 3D case are presented. In both cases the results demonstrate the efficacy of the proposed method.