Laser range data scan-matching algorithm for mobile robot indoor self-localization

This paper deals with a Scan-Matching algorithm for 2D laser range data developed to perform Self-Localization of mobile robots. The proposed algorithm is based on Features-Maps extracted from laser range data through Split and Merge features-extraction algorithm. A data association method, based on simultaneous observation of features from two consecutive scans, is used to determine correct features associations. Common features extracted from each scan are used as landmarks of the environment to estimate relative robot position in an unknown environment without considering any kinematic model of the robot and, moreover, without using odometry.