GeoPointing on indoor maps: enhancing compass sensor accuracy to enable interactive digital object selection in smartphone-based map applications

Indoor geopointing is a mobile computing field of application where high resolution compass data is required, in order to reliably identify digital objects on a map or within a scene. When mobile application developers wish to implement geopointing applications, they are hindered by the limited accuracy and reliability of the orientation sensors that are included in today's smartphones. Calibration issues and magnetic field interference are the two major influence factors that distort the data which is generated by the compass sensor. In this paper, we describe a combination of different techniques that we have developed to smoothen the orientation information that is produced by the compass sensor of an off-the-shelf smartphone. Our elaborations include filter functions that significantly enhance the orientation sensor's reliability and thus pave the way for reliable and stable geopointing applications.

[1]  H. Takemura,et al.  Tracking of user position and orientation by stereo measurement of infrared markers and orientation sensing , 2004, Eighth International Symposium on Wearable Computers.

[2]  Steven Strachan,et al.  It's a long way to Monte Carlo: probabilistic display in GPS navigation , 2006, Mobile HCI.

[3]  W FitzmauriceGeorge.  Situated information spaces and spatially aware palmtop computers , 1993 .

[4]  George W. Fitzmaurice,et al.  Situated information spaces and spatially aware palmtop computers , 1993, CACM.

[5]  Albrecht Schmidt,et al.  An Experimental Comparison of Physical Mobile Interaction Techniques: Touching, Pointing and Scanning , 2006, UbiComp.

[6]  Peter Fröhlich,et al.  GeoPointing : Evaluating the Performance of an Orientation Aware Location Based Service under Real-World Conditions , 2007 .

[7]  Lynne Baillie,et al.  Rolling, rotating and imagining in a virtual mobile world , 2005, Mobile HCI.

[8]  Robert B. McGhee,et al.  An extended Kalman filter for quaternion-based orientation estimation using MARG sensors , 2001, Proceedings 2001 IEEE/RSJ International Conference on Intelligent Robots and Systems. Expanding the Societal Role of Robotics in the the Next Millennium (Cat. No.01CH37180).

[9]  Peter Fröhlich,et al.  A mobile application framework for the geospatial web , 2007, WWW '07.

[10]  Jun Rekimoto,et al.  CyberCode: designing augmented reality environments with visual tags , 2000, DARE '00.

[11]  Thomas Grechenig,et al.  GeoPointing: evaluating the performance of orientation-aware location-based interaction under real-world conditions , 2008, J. Locat. Based Serv..

[12]  Hee-Jun Kang,et al.  A DCM Based Orientation Estimation Algorithm with an Inertial Measurement Unit and a Magnetic Compass , 2009, J. Univers. Comput. Sci..

[13]  Katashi Nagao,et al.  The world through the computer: computer augmented interaction with real world environments , 1995, UIST '95.

[14]  Pasi Välkkynen,et al.  Physical Browsing Research , 2005, PERMID.