Augmented EKF based SLAM method for improving the accuracy of the feature map

In this paper, we address a method for improving the accuracy of the feature map from the extended Kalman filter based SLAM (EKF SLAM) by estimating the systematic parameters of the robot. Most error of the robot while traveling is divided into two categories: systematic and non systematic error. The systematic error contributes much more to odometry errors than non systematic one on most smooth indoor surfaces. So, we appended the systematic parameters of the robot to the state vector of EKF SLAM as its elements and estimated the systematic parameters while performing the prediction and update state of EKF SLAM. Because the additional elements to be estimated are appended to the state vector of the EKF SLAM, this is called an augmented EKF SLAM (AEKF SLAM). Experimental result is presented to validate that our AEKF SLAM is able to generate a more accurate feature map than conventional EKF SLAM by decreasing odometric error of the robot.