The invention discloses a method for simultaneous localization and mapping of a mobile robot based on an improved particle filter. The method comprises the following steps: initializing an initial-moment pose of a robot; obtaining a t-moment prior probability density function according to the pose information at a t-1 moment, and generating a sampling particle set p; initializing the weights of particles; selecting an importance probability density function, generating a new sampling particle set q, calculating the weights of particles, updating the weights of the particles, and normalizing the weights; calculating the weighted sum of random sample particles at current moment t to express posterior probability density, and obtaining the moving pose and environmental map information; judging whether a new observed value is input; if so, returning; otherwise, ending the cycle; before returning, judging whether resampling is needed or not. According to the difference of the system state, a dynamic threshold is set for judgment, and a genetic algorithm is combined. According to the method disclosed by the invention, influence of a problem of particle degeneration on SLAM is reduced, and the calculated amount of the SLAM problem is reduced.