Usability of apple iPhones for inertial navigation systems

In recent decades, many indoor positioning techniques have been researched and some approaches have even been developed into consumer products. Most of them have been deployed into companies which benefit from indoor asset tracking. However, for public buildings like libraries or transportation systems, no direct benefit exists by installing expensive systems. Inertial navigation systems offer a form of positioning which is almost completely independent from external infrastructures, inexpensive and privacy friendly. As prices for sensors continuously drop, mobile terminals, such as cell phones or tablet PCs, are being equipped with various additional components, like GPS, cameras and light sensors, and moreover gyroscopes, compasses and accelerometers integration is also becoming commonplace. These last three components enable inertial navigation systems to calculate the position of the device. In this paper, we selected two devices, the iPhone 3GS and the iPhone 4, to analyze their sensors for usability of an inertial navigation system. For each device a common strapdown algorithm was implemented and varying standard filters applied to clean the output data stream of the sensors. Finally, we present the results, which are diverse according to the devices.

[1]  Hugh F. Durrant-Whyte,et al.  Initial calibration and alignment of low-cost inertial navigation units for land vehicle applications , 1999, J. Field Robotics.

[2]  Oliver J. Woodman,et al.  An introduction to inertial navigation , 2007 .

[3]  Greg Welch,et al.  Welch & Bishop , An Introduction to the Kalman Filter 2 1 The Discrete Kalman Filter In 1960 , 1994 .

[4]  Mohinder S. Grewal,et al.  Kalman Filtering: Theory and Practice Using MATLAB , 2001 .

[5]  Patrick Gallais,et al.  Atmospheric Re-Entry Vehicle Mechanics , 2007 .

[6]  Axel Küpper Location-based Services: Fundamentals and Operation , 2005 .

[7]  D. Lingaiah Kalman filtering: Theory and practice using MATLAB, 2nd ed [Book Review] , 2003, IEEE Circuits and Devices Magazine.

[8]  Naokazu Yokoya,et al.  Localization of Walking or Running User with Wearable 3D Position Sensor , 2007, 17th International Conference on Artificial Reality and Telexistence (ICAT 2007).

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

[10]  P. Lucas,et al.  Inertial navigation system for mobile land vehicles , 1995, 1995 Proceedings of the IEEE International Symposium on Industrial Electronics.

[11]  Robert Harle,et al.  Pedestrian localisation for indoor environments , 2008, UbiComp.

[12]  Henk L. Muller,et al.  Personal position measurement using dead reckoning , 2003, Seventh IEEE International Symposium on Wearable Computers, 2003. Proceedings..

[13]  Naokazu Yokoya,et al.  User Localization Using Wearable Electromagnetic Tracker and Orientation Sensor , 2006, 2006 10th IEEE International Symposium on Wearable Computers.

[14]  S. Godha,et al.  Foot mounted inertial system for pedestrian navigation , 2008 .