Research On SLAM Algorithm Based On Whale Swarm Optimized Particle Filter

Simultaneous localization and mapping(SLAM)is a key technology for autonomous navigation of mobile robots and occupies an important position in the field of robotics research.At present,the traditional SLAM algorithm is applied in complex environments with reduced positioning and mapping accuracy and poor stability,which seriously affects practical applications.Therefore,how to achieve high-precision localization and mapping in complex environments is the focus of current research in the field of robotics.This paper focuses on the FastSLAM algorithm based on particle filter.The main research contents are as follows:To address the problem of particle degradation and depletion in particle filter localization algorithm,the idea of meta-heuristic optimization algorithm is drawn,and an improved whale swarm algorithm is introduced into particle filter,and a particle filter algorithm for improved whale swarm(IWS-PF)is proposed in this paper.The strategy of optimal neighborhood random perturbation is introduced to adjust the selection of optimal particle,and the adaptive weight factor is constructed to modify the position formula,which can optimize the distribution of particle after importance sampling and improve the phenomenon of particle degradation and depletion.After testing,it is found that compared with the standard PF,BA-PF and PCSA-PF algorithms,the filtering accuracy of the IWS-PF algorithm is improved by 21.4%,3.8%and 2.7%respectively.The simulation results show that the IWS-PF algorithm still has high filtering accuracy and can achieve accurate state estimation under the premise of a small number of given particles.To deal with the problems of low pose estimation accuracy and poor mapping in the FastSLAM algorithm,an improved filter is applied to FastSLAM,and the FastSLAM algorithm based on whale swarm optimized particle filter(IWS-FastSLAM)is proposed in this paper.The original filter in FastSLAM is replaced with an IWS-PF filter,and the particle position adaptive update method is used to improve the path estimation part of FastSLAM.Tested by SLAM simulator,compared to standard FastSLAM,BA-FastSLAM and PCSA-FastSLAM algorithms,the localization accuracy of the IWS-FastSLAM algorithm is improved by 43.4%,12.7%and 6.9%,respectively.The simulation results show that the IWS-FastSLAM algorithm has high pose,landmark estimation accuracy and good stability,and can achieve accurate positioning and mapping.A mobile robot platform based on the ROS operating system is used to test the performance of the improved algorithm in a real environment.It is experimentally verified that the improved algorithm is able to construct maps with higher accuracy and more complete details using fewer particles.This method provides a reliable reference for mobile robots to achieve more accurate autonomous positioning and navigation.