Research on SLAM Accuracy of Multi-Strategy Artificial Hummingbird Algorithm Optimized Particle Filter
Aiming at the problem that the resampling of particle filter algorithm leads to particle scarcity and the need to increase the number of particles to improve the estimation accuracy,A particle recombina-tion particle filter algorithm based on multi-strategy artificial hummingbird algorithm optimization is pro-posed.Firstly,the midperpendicular algorithm improves the convergence speed of the artificial hummingbird algorithm.Through its intelligent foraging mechanism,the optimal particle guides the particle set to move towards the high likelihood region,thereby improving estimation accuracy;Secondly,the particle density near the optimal particle is calculated in real-time.When the density exceeds the set regional searching threshold,a Levy flight strategy is introduced to expand the search space.When it exceeds the maximum density value,the iteration number is adaptively adjusted;Finally,in the resampling stage,the retained parti-cles after screening are recombined with the remaining particles to form new particles,thereby increasing particle diversity.The performance of the improved algorithm in SLAM was verified through simulation ex-periments,and the results showed that compared with the other three algorithms,the algorithm has higher accuracy and better robustness in pose and landmark estimation.
particle filterartificial hummingbird algorithmadaptive adjustmentmidperpendicular algo-rithmLevy flightsimultaneous localization and mapping(SLAM)
NETL
NSTL
万方数据
维普
