Ubiquitous positioning is considered to be a highly demanding application for today’s Location-Based Services (LBS). While satellite-based navigation has achieved great advances in the past few decades, positioning and navigation in indoor scenarios and deep urban areas has remained a challenging topic of substantial research interest. Various strategies have been adopted to fill this gap, within which vision-based methods have attracted growing attention due to the widespread use of cameras on mobile devices. However, current vision-based methods using image processing have yet to revealed their full potential for navigation applications and are insufficient in many aspects. Therefore in this paper, we present a hybrid image-based positioning system that is intended to provide seamless position solution in six degrees of freedom (6DoF) for location-based services in both outdoor and indoor environments. It mainly uses visual sensor input to match with geo-referenced images for image-based positioning resolution, and also takes advantage of multiple onboard sensors, including the built-in GPS receiver and digital compass to assist visual methods. Experiments demonstrate that such a system can greatly improve the position accuracy for areas where the GPS signal is negatively affected (such as in urban canyons), and it also provides excellent position accuracy for indoor environments.
References
[1]
Snavely, N.; Seitz, S.M.; Szeliski, R. Modeling the world from internet photo collections. Int. J. Comput. Vis. 2008, 80, 189–210.
[2]
Hofman-Wellenhof, B.; Legat, K.; Wieser, M. Navigation: Principles of Positioning and Guidance; Springer-Verlag: Wien, Austria; New York, NY, USA, 2003.
[3]
Schaffalitzky, F.; Zisserman, A. Multi-View Matching for Unordered Image Sets. Proceedings of the Seventh European Conference on Computer Vision (ECCV′02), Copenhagen, Denmark, 27 May–2 June 2002; pp. 414–431.
[4]
Goedeme, T.; Tuytelaars, T. Fast Wide Baseline Matching for Visual Navigation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, 27 June–2 July 2004; pp. 24–29.
[5]
Zhang, W.; Kosecka, J. Image Based Localization in Urban Environments. Proceedings of the Third International Symposium on 3D Data Processing, Visualization and Transmission, Chapel Hill, NC, USA, 14–16 June 2006.
[6]
Robertson, D.; Cipolla, R. An Image-Based System for Urban Navigation. Proceedings of the British Machine Vision Conference (BMVC), London, UK, 7–9 September 2004.
[7]
Davison, A.J. Real-time Simultaneous Localisation and Mapping with a Single Camera. Proceedings of the 9th International Conference on Computer Vision, Nice France, 13–16 October 2003.
[8]
Elinas, P.; Sim, R.; Little, J.J. SLAM: Stereo Vision SLAM Using the Rao-Blackwellised Particle Filter and a Novel Mixture Proposal Distribution. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA 2006), Orlando, FL, USA, 15–19 May 2006.
[9]
Przemyslaw, B.; Pawel, S. Enhancing positioning accuracy in urban terrain by fusing data from a gps receiver, inertial sensors, stereo-camera and digital maps for pedestrian navigation. Sensors 2012, 12, 6764–6801.
[10]
Chen, R.; Wang, Y.; Pei, L.; Chen, Y.; Virrantaus, K. 3D personal navigation in smartphone using geocoded images. GPS World 2012, 36–42.
[11]
Ruiz-Ruiz, A.J.; Lopez-de-Teruel, P.E.; Canovas, O. A Multisensory LBS Using SIFT-Based 3 D Models. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia, 13–15 November 2012.
[12]
Pei, L.; Chen, R.; Liu, J.; Liu, Z.; Kuusniemi, H.; Chen, Y.; Zhu, L. Sensor Assisted 3D Personal navigation on a Smart Phone in GPS Degraded Environments. Proceedings of the 19th International Conference on Geoinformatics, Shanghai, China, 24–26 June 2011.
[13]
Karimi, H. Universal Navigation on Smartphones; Spring: New York, NY, USA, 2011.
[14]
Jaspers, H.; Schauerte, B.; Fink, G.A. SIFT-Based Camera Localization Using Reference Objects for Application in Multi-camera Environments and Robotics. Proceedings of the International Conference on Pattern Recognition Applications and Methods, Vilamoura, Algarve, Portugal, 6–8 February 2012.
[15]
Chowdhary, G.; Johnson, E.; Magree, D.; Wu, D.; Shein, A. GPS-denied indoor and outdoor monocular vision aided navigation and control of unmanned aircraft. J. Field Robot. 2013, 30, 415–438.
[16]
Li, X.; Wang, J.; Yang, L. Outlier Detection for Indoor Vision-Based Navigation Applications. Proceedings of the 24th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS 2011), Portland, OR, USA, 19–23 September 2011.
[17]
Morel, J.; Yu, G. ASIFT: A new framework for fully affine invariant image comparison. SIAM J. Imaging Sci. 2009, 2, 438–469.
[18]
Harris, C.; Stephens, M. A Combined Corner and Edge Detector. Proceedings of the Fourth Alvey Vision Conference, Manchester, UK, 31 August–2 September 1988; pp. 147–151.
[19]
Lowe, D. Distinctive image features from scale invariant key points. Int. J. Comput. Vision. 2004, 60, 91–110.
[20]
Fischler, M.A.; Bolles, R.C. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 1981, 24, 381–395.
[21]
Wu, C. VisualSFM: A Visual Structure from Motion System. Available online: http://www.cs.washington.edu/homes/ccwu/vsfm/ (on accessed 15 December 2012).
[22]
Luhmann, T.; Robson, S.; Kyle, S.; Harley, I. Close Range Photogrammetry: Principles, Methods and Application; Whittles Publishing: Scotland, UK, 2006; p. 206.
[23]
Luhmann, T. Precision potential of photogrammetric 6DOF pose estimation with a single camera. ISPRS J. Photogramm. Remote Sens. 2009, 64, 275–284.
[24]
Li, X.J.; Wang, J.L. Image Matching Techniques for Vision-based Indoor Navigation Systems: Performance Analysis for 3 D Map Based Approach. Proceedings of the International Conference on Indoor Positioning and Indoor Navigation, Sydney, Australia, 13–15 November 2012.
