Pedestrian Indoor Localization Using Foot Mounted Inertial Sensors in Combination with a Magnetometer, a Barometer and RFID

A system for pedestrian indoor localization is presented, which uses the data of an inertial sensor unit mounted on the foot of a person walking through an indoor or outdoor environment. The inertial sensor data are integrated to a position/orientation information using a classical strapdown navigation approach, while several additional sensor data and constraints, such as Zero Velocity Updates, magnetometer and barometer readings and the detection of spatially distributed RFID tags, are incorporated to the solution using an Unscented Kalman Filter. The work presents a custom sensor system development, describes the developed algorithms and evaluates several methods to reduce the drift, which usually comes with the integration of low cost inertial sensors.

[1]  Shahid Ali,et al.  A Novel Indoor Location Sensing Mechanism for IEEE 802.11 b/g Wireless LAN , 2007, 2007 4th Workshop on Positioning, Navigation and Communication.

[2]  Rudolph van der Merwe,et al.  Sigma-point kalman filters for probabilistic inference in dynamic state-space models , 2004 .

[3]  Zoubir Irahhauten,et al.  Analysis of a UWB Indoor Positioning System Based on Received Signal Strength , 2007, 2007 4th Workshop on Positioning, Navigation and Communication.

[4]  Ozkan Bebek,et al.  Personal navigation via shoe mounted inertial measurement units , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  Patrick Robertson,et al.  Integration of foot-mounted inertial sensors into a Bayesian location estimation framework , 2008, 2008 5th Workshop on Positioning, Navigation and Communication.

[6]  Jong-Suk Choi,et al.  Advanced indoor localization using ultrasonic sensor and digital compass , 2008, 2008 International Conference on Control, Automation and Systems.

[7]  Hugh F. Durrant-Whyte,et al.  A new method for the nonlinear transformation of means and covariances in filters and estimators , 2000, IEEE Trans. Autom. Control..

[8]  Y. Manoli,et al.  Efficient orientation estimation algorithm for low cost inertial and magnetic sensor systems , 2009, 2009 IEEE/SP 15th Workshop on Statistical Signal Processing.

[9]  Wolfram Burgard,et al.  Probabilistic Robotics (Intelligent Robotics and Autonomous Agents) , 2005 .

[10]  G. Retscher,et al.  Location Determination in Indoor Environments for Pedestrian Navigation , 2006, 2006 IEEE/ION Position, Location, And Navigation Symposium.

[11]  W. Rosenstiel,et al.  RIL — reliable RFID based indoor localization for pedestrians , 2008, 2008 16th International Conference on Software, Telecommunications and Computer Networks.

[12]  F. Seco,et al.  A comparison of Pedestrian Dead-Reckoning algorithms using a low-cost MEMS IMU , 2009, 2009 IEEE International Symposium on Intelligent Signal Processing.

[13]  Hyo-Sung Ahn,et al.  Indoor mobile robot and pedestrian localization techniques , 2007, 2007 International Conference on Control, Automation and Systems.

[14]  Fernando Seco Granja,et al.  Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.

[15]  Eric Foxlin,et al.  Pedestrian tracking with shoe-mounted inertial sensors , 2005, IEEE Computer Graphics and Applications.

[16]  Dieter Fox,et al.  Bayesian Filtering for Location Estimation , 2003, IEEE Pervasive Comput..

[17]  Neal Lesh,et al.  Indoor navigation using a diverse set of cheap, wearable sensors , 1999, Digest of Papers. Third International Symposium on Wearable Computers.

[18]  Rudolph van der Merwe,et al.  Sigma-Point Kalman Filters for Integrated Navigation , 2004 .

[19]  Angelo Maria Sabatini.  Dead-Reckoning Method for Personal Navigation Systems Using Kalman Filtering Techniques to Augment Inertial/Magnetic Sensing , 2009 .

[20]  Jeff Bird,et al.  Indoor navigation with foot-mounted strapdown inertial navigation and magnetic sensors [Emerging Opportunities for Localization and Tracking] , 2011, IEEE Wireless Communications.

[21]  Young Jin Nam,et al.  Efficient Indoor Localization and Navigation with a Combination of Ultrasonic and CSS-based IEEE 802.15.4a , 2009, Proceedings of the 4th International Conference on Ubiquitous Information Technologies & Applications.

[22]  Ian Milligan,et al.  Indoor localization using pedestrian dead reckoning updated with RFID-based fiducials , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[23]  Yiannos Manoli,et al.  A modular and mobile system for indoor localization , 2010, 2010 International Conference on Indoor Positioning and Indoor Navigation.

[24]  Yiannos Manoli,et al.  Multi-modal sensor data and information fusion for localization in indoor environments , 2010, 2010 7th Workshop on Positioning, Navigation and Communication.