Aiming at the problems of slower particle convergence and poor positioning accuracy when using traditional Monte Carlo positioning algorithms in the navigation and positioning process of mobile robots, as well as low relocation efficiency after artificial kidnapping, this article gives an improved Particle filter positioning method to improve the navigation and positioning efficiency of mobile robots. First of all, it is improved on the basis of the Monte Carlo positioning algorithm and integrated into the method of adaptive region division to ensure that the region contains more effective information, reduce the convergence time of particles, and complete the preliminary coarse positioning of the robot. Then, in the particle sampling and resampling stage, the normal distribution probability model is used to update the particle weights to achieve faster and more efficient global positioning. Through experimental comparison and analysis, compared with the Monte Carlo positioning algorithm, the given method has shortened the time consumption by 4s, and the adaptive Monte Carlo positioning method in this paper can keep the positioning error at about 6cm, thus verifying the given method Effectiveness and stability.