کنفرانس بین المللی کنترل ، ابزار دقیق و اتوماسیون

صفحه اصلی / نهمین کنفرانس بین المللی کنترل ، ابزار دقیق و اتوماسیون

Improved Unscented Kalman Filter Algorithm to Increase the SLAM Accuracy

نویسندگان :

Hasan Enami Eraghi¹ Mohammadreza Taban² Sayed Farzad Bahreinian³

1- دانشگاه صنعتی اصفهان 2- دانشگاه صنعتی اصفهان 3- دانشگاه صنعتی اصفهان

کلمات کلیدی :

Information matrix،Mobile robots،Simultaneous Localization and Mapping،and Unscented Kalman Filter

چکیده :

Determining the position of autonomous mobile robots, especially when an external positioning reference like a satellite-based positioning signal is unavailable, poses a significant challenge. In such situations, the robot must make a map of its surrounding environment and determine its own position on the map at the same time. Therefore, the best way to find the position of the robot in unknown environments is using Simultaneous Localization and Mapping (SLAM) techniques. In this paper an Improved Unscented Kalman Filter SLAM (IUKF-SLAM) is presented for improving the accuracy, consistency and convergency of the Unscented Kalman Filter (UKF) applied in the SLAM. In this method, the information matrix and the information state vector are calculated by the inverse of covariance matrix and transformation of the state vector, respectively, in the prediction step. Then they are updated in the update step. Simulation results indicate that the proposed method is more accurate than that of EKF and UKF SLAMs.