ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

In the simultaneous localization and mapping (SLAM) technology, mobile robots generally has problems such as low state accuracy, poor stability, and complicated calculation, which can not meet the requirements of real-time and accuracy in the SLAM process. In order to improve this problem, an SLAM autonomous positioning algorithm was proposed based on iterated square root central difference Kalman filter (ISRCDKF). The central difference transform was used to deal with the nonlinear problem of SLAM, avoiding complex operations such as Jacobian matrix in the Taylor formula expansion, and directly transmitting the square root factor reduction algorithm of the covariance matrix in the filter update process. In the complexity, the Levenberg-Marquardt (L-M) optimization method was used to introduce the real-time modified covariance matrix of the adjustment parameters in the iterated observation update process to improve the accuracy and stability of the algorithm. The simulation results showed that under the same data model and noise environment, the proposed ISRCDKF-SLAM algorithm was compared with SLAM algorithm based on extended Kalman filter (EKF-SLAM),SLAM algorithm based on unscented Kalman filter (UKF-SLAM) and SLAM algorithm based on cubature Kalman filter (CKF-SLAM), the root mean square error was reduced by 47.3%, 32.7% and 25.0%, respectively. At the same time, compared with the UKF-SLAM algorithm and CKF-SLAM algorithm with the same computational complexity, the running time of the proposed algorithm was reduced by 15.1% and 10.8%, respectively, which proved the effectiveness of the algorithm. Finally, the proposed algorithm was embedded into the mobile robot platform for field experiment verification, which further proved the practicability and effectiveness of the algorithm.