The Research On Algorithms Of Mobile Robot Simultaneous Localization And Mapping

In the process of social development,in order to meet the needs of human life,intelligent machines and equipment are getting more and more attention from humans,such as intelligent mobile robots.In the study of mobile robots,Simultaneous location and mapping is the basis of mobile robot research.How to accurate the mobile robot's location is the core of the direction.This paper is mainly about how to improves the accuracy and real-time of mobile robot's in simultaneous location and mapping.The research contents are as follows:Firstly,the background significance of the research is systematically summarized,and the research status of the robot,the prospect of the development and the system model needed for the subject are analyzed.Secondly,in order to solve the problem of poor estimation accuracy of extended Kalman filter in nonlinear system,linear fitting Kalman filter is introduced,and the algorithm is based on random variable Sigma point weighted least squares which is used to estimate a linear fitting function.Instead of Extended Kalman in Taylor first order truncation.The simulation results show that the algorithm has the same estimation accuracy as the unscented Kalman filter,but the computational complexity is obviously reduced.Thirdly,for the non-linear and non-Gaussian system environment in which the mobile robot Simultaneous location and mapping,the unscented Kalman filter algorithm without Jacobian matrix is used to recursively estimate the pose of the robot.As an important function of the particle,the particle is selected for the maintenance of the environmental characteristics.When the number of sampling point is large,the computational complexity is large.Using n-dimensional spherical single-shaped sampling points instead of the traditional Sigma point sampling.Introducing the parameters to calculate the weight,and it is a good aggregation of the sampling points.Finally,H~?filter is introduced to improve the robustness of the algorithm.Finally,in order to carry on the feature maintenance to the particle filter in FastSLAM,the number of particles is too many and the calculation time is too long.The symmetry KL distance is introduced,and the particles are randomly divided into two groups of particles,and the information distance between the two is calculated,By comparing with the threshold to determine the number of particles in the next time to increase or decrease,To achieve the adaptive number of particles,it is effectively reduce the filtering time complexity.The improved algorithm can reduce the computational complexity under certain precision conditions.