AGV global localization using indistinguishable artificial landmarks

In this paper we consider the global localization problem for an industrial AGV moving in a known environment. The problem consists of determining the pose of the vehicle without any prior information about its location. The vehicle is supposed to be equipped with a laser scanner that allows to measure the range and bearing of the vehicle with respect to a set of anonymous landmarks. A map with the positions of all landmarks in the environment is available to the localization system. We propose a novel algorithm for AGV self-localization based on landmarks identification that can take into account also false detections, very common in industrial environments. The pose is computed with a single scan (2D), without any sensor fusion. The performance of the proposed strategy is shown both by simulations and experiments on real industrial plants.

[1]  Wolfram Burgard,et al.  Mapping and localization with RFID technology , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[2]  Mignon Park,et al.  Vision-based global localization for mobile robots with hybrid maps of objects and spatial layouts , 2009, Inf. Sci..

[3]  Wolfram Burgard,et al.  Robust Monte Carlo localization for mobile robots , 2001, Artif. Intell..

[4]  Ilan Shimshoni,et al.  Reliable and efficient landmark-based localization for mobile robots , 2010, Robotics Auton. Syst..

[5]  Francisco Bonin-Font,et al.  Visual Navigation for Mobile Robots: A Survey , 2008, J. Intell. Robotic Syst..

[6]  W. Burgard,et al.  Markov Localization for Mobile Robots in Dynamic Environments , 1999, J. Artif. Intell. Res..

[7]  Xu Yubin,et al.  TOA Estimate Algorithm Based UWB Location , 2009, 2009 International Forum on Information Technology and Applications.

[8]  Wolfram Burgard,et al.  Monte Carlo Localization: Efficient Position Estimation for Mobile Robots , 1999, AAAI/IAAI.

[9]  Bradford W. Parkinson,et al.  Autonomous GPS Integrity Monitoring Using the Pseudorange Residual , 1988 .

[10]  F. Crosilla,et al.  AUTOMATIC POINT MATCHING OF GIS GEOMETRIC FIGURES , 2004 .

[11]  Christof Röhrig,et al.  Localization of an omnidirectional transport robot using IEEE 802.15.4a ranging and laser range finder , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[12]  Sebastian Thrun,et al.  Probabilistic robotics , 2002, CACM.

[13]  Lothar Schulze,et al.  Automated Guided Vehicle Systems: a Driver for Increased Business Performance , 2008 .

[14]  Avinash C. Kak,et al.  Vision for Mobile Robot Navigation: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Bernardo Wagner,et al.  Robust Self-Localization in Industrial Environments based on 3D Ceiling Structures , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  Francisco Sandoval Hernández,et al.  Natural landmark extraction for mobile robot navigation based on an adaptive curvature estimation , 2008, Robotics Auton. Syst..

[17]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[18]  R. Reulke,et al.  Remote Sensing and Spatial Information Sciences , 2005 .

[19]  Alonzo Kelly,et al.  Field and service applications - An infrastructure-free automated guided vehicle based on computer vision - An Effort to Make an Industrial Robot Vehicle that Can Operate without Supporting Infrastructure , 2007, IEEE Robotics & Automation Magazine.

[20]  Hugh F. Durrant-Whyte,et al.  Data association for mobile robot navigation: a graph theoretic approach , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).