Sigma Point Filter Based Mobile Robot Simultaneous Localization And Mapping Algorithms

Mobile robot Simultaneous Localization and Mapping (SLAM) problem is one of the most active research areas in mobile robotics. As the base of navigation, obstacle avoidance, path planning and other tasks, it determines the realization of truly autonomous for mobile robot in unknown environment. SLAM is the process of building a map of an unknown environment with onboard sensors, while at the same time determining the pose of the mobile robot within this map. The process concerns several aspects including sensor techniques, map representation and algorithm realization. And the SLAM algorithm realization is one of the most important aspects.Considering the nonlinear characteristics of the motion model and observation model in SLAM, Sigma point filter is introduced into SLAM algorithms. In this dissertation, Sigma point Kalman filter (SPKF), including unscented Kalman filter (UKF) and central difference Kalman filter (CDKF) SLAM algorithms with different sampling rules are studied. The properties including accuracy, consistency and computational complexity of these algorithms are analyzed and compared. Several improved algorithms are proposed to improve accuracy, computational efficiency, robustness respectively and to extend SLAM application domains. The innovation of this dissertation is as follows.(1) A square root CDKF (SR-CDKF) SLAM algorithm is presented. By using QR factorization and Cholesky update to get the square root of the state covariance matrix directly, the computational efficiency is impoved.(2) A computational complexity reduced CDKF (CR-CDKF) SLAM algorithm is proposed. It is presented in the context of the linear regression Kalman filter. An improved sampling strategy is given by reconstructing the estimated state and its covariance during prediction, observation update and map augmentation process. The computational complexity of this algorithm is reduced to O(n2). The idea of compressed filter is then used in the above algorithm and a compressed CDKF SLAM algorithm is proposed. The computational complexity is further reduced which makes it more suitable for the application in large scale environment.(3) An optimized iterated SPKF (O-ISPKF) SLAM algorithm is proposed. The damped Gauss-Newton iteration is adopted by introducing the parameter ? and the corresponding condition during the observation update process. The proposed algorithm is proved to be stable and be able to improve the accuracy of SPKF SLAM algorithm effectively.(4) The affections of parameter y in nolinear H?filter SLAM algorithms to the estimated accuracy and convergence are discussed. An improved Sigma point H?, filter (SPHF) SLAM algorithm which employs the improved sampling strategy is presented. It has lower computational complexity and better robustness than SPKF SLAM algorithm.All of the proposed algorithms are proved to be effective through the simulation experiments of different environments and also through the experiments on the standard datasets for SLAM algorithms' evaluation.