Particle Merging Resampling Based Monte Carlo Localization for Mobile Robot

A merge Monte Carlo localization(Merge-MCL) method for mobile robot based on particle merging resampling is presented.Grid cells and grid sets are established to represent the workspace of mobile robot,and then a particle merging technique based on the particles' spatial similarity is proposed.The technique adapts the number of particles according to the rational distribution of spatial particles.Resampling using the particle merging technique mitigates the weight degeneracy problem of particles and avoids diversity impoverishment caused by the traditional resampling methods.Simulation results illustrate that the particle merging resampling can adapt the number of particles efficiently and the Merge-MCL method is efficient and robust.