The Research On Algorithm Of Mobile Robots Simultaneous Localization And Map-Building

With the rapid development of technology, the research on intelligent robot has become the hot issue of robotic fields. Autonomous navigation ability is the important embodiment of robot intelligence, and simultaneous localization and map building is the key technique of realizing true autonomous navigation. Therefore, the study of simultaneous localization and map building has important theoretical and practical implications. The main work of this paper is listed as follows:Firstly, the research background and significance are briefly summarized. The research status of simultaneous localization and map building are introduced, and mobile robot system models are analyzed.Secondly, the main algorithms of realizing simultaneous localization and map building are discussed, and the advantages and drawbacks of those algorithms are compared.Again, aiming at the particle degradation problem of an mobile robot Fast SLAM a chaos optimization MPSO based algorithm was proposed. With this method, an improved particle group optimization ideas introduced to mobile robot simultaneous localization and map building solve the problem of particle degradation. Simulation results show that, to improve the particle sampling efficiency and the accuracy of the algorithm to create a map. Simulation results show that, this algorithm has higher estimation accuracy.Finally, the unscented Kalman filter in numerical instability and residual information asymmetry of the problem and put forward strong tracking square root unscented Kalman adaptive mobile robot simultaneous localization and map building algorithm based on normalized residuals. Using the square root filtering to ensure the covariance matrix is positive definite and the residual was normalized, so as to improve the positioning accuracy of the system.