Constrained Kalman filtering for indoor localization of transport vehicles using floor-installed HF RFID transponders

Localization of transport vehicles is an important issue for many intralogistics applications. The paper presents an inexpensive solution for indoor localization of vehicles. Global localization is realized by detection of RFID transponders, which are integrated in the floor. The paper presents a novel algorithm for fusing RFID readings with odometry using Constraint Kalman filtering. The paper presents experimental results with a Mecanum based omnidirectional vehicle on a NaviFloor® installation, which includes passive HF RFID transponders. The experiments show that the proposed Constraint Kalman filter provides a similar localization accuracy compared to a Particle filter but with much lower computational expense.

[1]  Mathias Baum.  Passive 13 . 56 MHz RFID Transponders for Vehicle Navigation and Lane Guidance , 2007 .

[2]  L. Meier.  Estimation and control with quantized measurements , 1971 .

[3]  Thomas Kämpke,et al.  Exploiting RFID Capabilities Onboard a Service Robot Platform , 2012, Towards Service Robots for Everyday Environments.

[4]  Axel Steinhage,et al.  SensFloor® and NaviFloor®: Large-Area Sensor Systems beneath Your Feet , 2011 .

[5]  Shigeki Sugano,et al.  Pose estimation of a mobile robot on a lattice of RFID tags , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[6]  Andre Heller,et al.  Global Localization and Position Tracking of Automatic Guided Vehicles using passive RFID Technology , 2014, ISR 2014.

[7]  Eric Guizzo,et al.  Three Engineers, Hundreds of Robots, One Warehouse , 2008, IEEE Spectrum.

[8]  G. Hoblos,et al.  State estimation under nonlinear state inequality constraints. A tracking application , 2008, 2008 16th Mediterranean Conference on Control and Automation.

[9]  Ju-Jang Lee,et al.  A Hierarchical Algorithm for Indoor Mobile Robot Localization Using RFID Sensor Fusion , 2011, IEEE Transactions on Industrial Electronics.

[10]  Axel Hahn,et al.  A fully-automated manufacturing environment realized through a flexible in house logistic system with smart transportation infrastructure , 2014 .

[11]  Cristian Secchi,et al.  An Inertial/RFID Based Localization Method for Autonomous Lawnmowers , 2012, SyRoCo.

[12]  Andreas Nettstraeter,et al.  The Internet of Things in Logistics , 2010 .

[13]  Francesco Martinelli,et al.  A Passive UHF-RFID System for the Localization of an Indoor Autonomous Vehicle , 2012, IEEE Transactions on Industrial Electronics.

[14]  Youngsu Park,et al.  An efficient localization method using RFID tag floor localization and dead reckoning , 2012, 2012 12th International Conference on Control, Automation and Systems.

[15]  D. Simon.  Kalman filtering with state constraints: a survey of linear and nonlinear algorithms , 2010 .

[16]  Francesco Martinelli,et al.  Constrained and quantized Kalman filtering for an RFID robot localization problem , 2010, Auton. Robots.

[17]  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.

[18]  Yu Liu,et al.  Resource Management with RFID Technology in Automatic Warehouse System , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.