Mobile robot localization based on particle filter
Fengbing Luo, Bianjing Du, Zhen Hong Fan · 2014
This paper proposes a self-localization algorithm for mobile robot based on particle filter algorithm. It uses the Monte Carlo method to solve the integral operation of the Bayesian estimation. In order to make the self-localization algorithm real-time, the sequence of importance sampling (SIS) method is introduced. Considering the actual environment, the grid map modal is created. In the inspection process of the robot, Environment map is updated by the Monte Carlo algorithm. This paper designs probability motion modal, detection modal and observation modal of robot, and make a simulation test. The results show that when the robot is on patrol, it can know its position and update the environment map in real time.