Evaluation of Algorithms for indoor mobile robot self-localization through laser range finders data

Abstract In this paper an evaluation of several indoor mobile robot localization algorithms is presented. Two-dimensional laser range scans are used to estimate motions without a kinematic model of the robot and, moreover, without using odometry. Features extracted from each scan are used as landmarks of the environment to estimate relative robot position in an unknown environment. Different known feature extraction algorithms have been deeply tested in order to evaluate their performances. A data association method uses simultaneous observation of features from two consecutive scans to determine correct features associations. Two different strategies are tested to estimate relative robot position using common features of two consecutive scans. All of these algorithms have been tested with two different angular resolutions of the laser scanner, and a comparison between all of them, in term of number of features identified and execution-time, is presented.

[1]  C.-T. Chen,et al.  Indoor localization using line based map for autonomous mobile robot , 2008, 2008 IEEE Workshop on Advanced robotics and Its Social Impacts.

[2]  G. Muscato,et al.  A new global localization algorithm based on feature extraction and particle filter , 2006, 2006 14th Mediterranean Conference on Control and Automation.

[3]  Giovanni Muscato,et al.  An approach to global localization problem using mean shift algorithm , 2007 .

[4]  Eduardo Mario Nebot,et al.  Localisation in large-scale environments , 2001, Robotics Auton. Syst..

[5]  Ingemar J. Cox,et al.  Dynamic Map Building for an Autonomous Mobile Robot , 1990, EEE International Workshop on Intelligent Robots and Systems, Towards a New Frontier of Applications.

[6]  Giovanni Muscato,et al.  STRUCTURE AND MODEL IDENTIFICATION OF A VORTEX-BASED SUCTION CUP , 2008 .

[7]  Giovanni Muscato,et al.  A feedback control system for a rapid production process based on robotic welding deposition , 2010, ISR/ROBOTIK.

[8]  Li Zhang,et al.  Line segment based map building and localization using 2D laser rangefinder , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[9]  Gregory Dudek,et al.  Precise positioning using model-based maps , 1994, Proceedings of the 1994 IEEE International Conference on Robotics and Automation.

[10]  Geovany de Araújo Borges,et al.  A split-and-merge segmentation algorithm for line extraction in 2D range images , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[11]  Giovanni Muscato,et al.  AN INNOVATIVE AUTONOMOUS OUTDOOR VEHICLE BASED ON MICROSOFT ROBOTIC STUDIO , 2010 .

[12]  L. Cantelli,et al.  A modular software interface for the control system of an arc welding robot , 2009, 2009 2nd Conference on Human System Interactions.

[13]  Ingemar J. Cox,et al.  Dynamic Map Building for an Autonomous Mobile Robot , 1992 .

[14]  Benjamin Kuipers,et al.  A robot exploration and mapping strategy based on a semantic hierarchy of spatial representations , 1991, Robotics Auton. Syst..

[15]  Bernt Schiele,et al.  A comparison of position estimation techniques using occupancy grids , 1994, Robotics Auton. Syst..

[16]  James L. Crowley.  World modeling and position estimation for a mobile robot using ultrasonic ranging , 1989, Proceedings, 1989 International Conference on Robotics and Automation.

[17]  Giovanni Muscato,et al.  MODELLING OF AN INNOVATIVE ACTUATOR FOR CLIMBING ROBOT ADHESION , 2009 .

[18]  Ewald von Puttkamer,et al.  Keeping track of position and orientation of moving indoor systems by correlation of range-finder scans , 1994, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94).