Research on Vector Weighted Average Adaptive Adjustment of SLAM Accuracy
Aiming at the problem that particle filter algorithm needs a sea of particles to improve accuracy and the loss of particle diversity caused by resampling,a particle recombination particle filter algorithm op-timized by auto-adjustment INFO(weighted mean of vectors algorithm)is proposed.Firstly,the optimal particle guides the particle set to move to the desired region through the different weighted average rules of vectors,so as to improve the estimation accuracy;Secondly,the particle density around the optimal particle is calculated in real time.When the density is higher than the threshold,the number of iterations is adjusted adaptively,and the particle density is monitored in real time.According to this index,the sub-optimal parti-cle is introduced to adjust the particle set distribution adaptively;Finally,the resampling stage recombines the retained particles and the remaining particles into new particles to increase particle diversity.Simulation experiments are tested to verify the performance of the improved algorithm in SLAM.The results show that compared with the norm algorithm,the algorithm has higher accuracy and better stability in the calculation of pose and landmark.
particle filterweighted mean of vectors algorithmadaptive adjustmentsimultaneous localiza-tion and mapping(SLAM)
NETL
NSTL
万方数据
维普
