An Improved Localization Algorithm for Intelligent Robot

Abstract

The application of intelligent robot is more and more extensive, and self-localization is the key technology of intelligent robot. When using the odometer or lidar for positioning, the wheeled intelligent robot has large error. In order to solve this problems, an adoptive Monte Carlo localization algorithm based on particle filter is proposed by combining the data of binocular camera and lidar. In the prediction stage, the data of binocular camera and lidar are used to improve the proposed distribution to reduce the number of particles. And the posterior probability distribution of robot pose is estimated with fewer particles. In order to verify the performance of the improved algorithm, experiments are carried out on a four-wheel intelligent robot platform. The results show that the improved AMCL algorithm can effectively improve the localization accuracy of the robot and the improved AMCL algorithm has good practicability.