Improvement of Robot's Simultaneous Localization and Mapping Using an Effective Transformation to Achieve Linear Model

arXiv:2606.28475v1 Announce Type: new Abstract: Nowadays mobile robots have wide engineering applications. Simultaneous localization and mapping (SLAM) is an important task of these robots. The major and common algorithms used for this task are based on extended Kalman filter (EKF). One of the main problems in EKF-based SLAM is its divergence. The nonlinearity of motion and observation models and linearization error are the main reasons for the divergence. There have been some efforts to address...