6DOF localization with AUKF based on triple RTK-GNSS

Abstract

SLAM (simultaneous localization and mapping) plays a crucial role in autonomous navigation. In a previous study, SLAM based on AUKF (augmented unscented Kalman filter), called AUKF-SLAM, was proposed. This study demonstrated that simultaneous estimation of kinematic parameters improves the accuracy on 2D SLAM in an indoor environment. We currently aim to develop the 3D AUKF-SLAM for outdoor use, and this paper presents the 6DOF localization based on AUKF as a preliminary step. To expand 2D (3DOF) localization to 6DOF localization, we adopted quaternion for attitude representation. However, it is not the best way to estimate each element of the four-dimensional vector of quaternion as state variables because they do not vary independently. As a solution to this problem, the idea of estimating the attitude error represented as the three-dimensional parameter called GRPs (generalized Rodrigues parameters) was proposed. In addition, it was reported that simultaneous estimation of the attitude error represented as GRPs and states not represented as errors is effective for the estimation of the motion of space crafts. Therefore, we applied this method to wheeled mobile robots to address the problem and realize the 6DOF localization based on AUKF. We implemented this system on ROS (robot operating system) and experimented in simulational and real environments. As a result, we demonstrated that it could perform 6DOF localization and estimation of the wheel radius simultaneously.