Monte Carlo localization based on off-line feature matching and improved particle swarm optimization for mobile robots

To achieve the autonomy of mobile robots, effective localization is an essential process. Among localization algorithms, the Adaptive Monte Carlo Localization (AMCL) algorithm is most commonly used in many indoor environments. However, when the initial position is unknown, the efficiency and success rate of localization based on the AMCL algorithm decrease with the increasing area of the map. In this paper, an improved MCL algorithm named off-line feature matching and improved particle swarm optimization for Monte Carlo Localization (OFM-IPSO MCL) is proposed. Feature matching is adopted to reduce the online computational burden. Compared with the AMCL algorithm, OFM-IPSO MCL shows better results in the problems of positioning without initial pose and kidnapping robot by using a small number of particles. For positioning without an initial pose, the OFM-IPSO algorithm uses the feature extraction and feature matching methods to find the possible positions of the robot. In the problem of kidnapping robot, a method for determining if the robot has been "kidnapped" is proposed, which determines whether the robot has lost its pose. The validity and efficiency of the OFM-IPSO MCL algorithm are demonstrated by the Robotic Operating System (ROS). Extensive results and comparisons are also provided in this paper.