On Adaptive Monte Carlo Localization Algorithm for the Mobile Robot Based on ROS

This paper proposes an adaptive Monte Carlo location (MCL) algorithm in stages to improve the common problems existed in the traditional MCL method, such as the high computational complexity, and the hijacked circumstance for the mobile robot. Firstly, the current positioned state, namely global localization or local localization, is judged. Secondly, different particles are assigned to conduct localization and tracking based on the different status of positioning allocation. In addition, the accuracy of the positioning system is used to detect whether the robot has been hijacked or not. The research of SLAM carried out on the Robot Operating System (ROS) shows that the proposed method is effective in achieving an accurate localization and reducing the computational complexity. It is suitable for indoor mobile robot SLAM.