How Computer Vision Can Help in Outdoor Positioning

Localization technologies have been an important focus in ubiquitous computing. This paper explores an underrepresented area, namely computer vision technology, for outdoor positioning. More specifically we explore two modes of positioning in a challenging real world scenario: single snapshot based positioning, improved by a novel high-dimensional feature matching method, and continuous positioning enabled by combination of snapshot and incremental positioning. Quite interestingly, vision enables localization accuracies comparable to GPS. Furthermore the paper also analyzes and compares possibilities offered by the combination of different subsets of positioning technologies such as WiFi, GPS and dead reckoning in the same real world scenario as for vision based positioning.

[1]  Quentin Ladetto,et al.  Digital Magnetic Compass and Gyroscope Integration for Pedestrian Navigation , 2002 .

[2]  G LoweDavid,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004 .

[3]  A. Ruina,et al.  Multiple walking speed-frequency relations are predicted by constrained optimization. , 2001, Journal of theoretical biology.

[4]  Gaetano Borriello,et al.  Location Systems for Ubiquitous Computing , 2001, Computer.

[5]  Cordelia Schmid,et al.  A Performance Evaluation of Local Descriptors , 2005, IEEE Trans. Pattern Anal. Mach. Intell..

[6]  Gaetano Borriello,et al.  Particle Filters for Location Estimation in Ubiquitous Computing: A Case Study , 2004, UbiComp.

[7]  Bill N. Schilit,et al.  Place Lab: Device Positioning Using Radio Beacons in the Wild , 2005, Pervasive.

[8]  John Krumm,et al.  Accuracy characterization for metropolitan-scale Wi-Fi localization , 2005, MobiSys '05.

[9]  Wei Zhang,et al.  Image Based Localization in Urban Environments , 2006, Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06).

[10]  C. Tom Judd,et al.  A Personal Dead Reckoning Module , 1997 .

[11]  Vincent Gabaglio.  GPS/INS integration for pedestrian navigation , 2003 .

[12]  Roberto Cipolla,et al.  An Image-Based System for Urban Navigation , 2004, BMVC.

[13]  Konrad Tollmar,et al.  Searching the Web with mobile images for location recognition , 2004, CVPR 2004.

[14]  Wei Zhang,et al.  Hierarchical building recognition , 2007, Image Vis. Comput..

[15]  H. Haas,et al.  Pedestrian Dead Reckoning : A Basis for Personal Positioning , 2006 .

[16]  Roberto Cipolla,et al.  A system for automatic pose-estimation from a single image in a city scene , 2002 .

[17]  Jiri Matas,et al.  Robust wide-baseline stereo from maximally stable extremal regions , 2004, Image Vis. Comput..

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

[19]  Takeshi Kurata,et al.  Personal positioning based on walking locomotion analysis with self-contained sensors and a wearable camera , 2003, The Second IEEE and ACM International Symposium on Mixed and Augmented Reality, 2003. Proceedings..

[20]  Luc Van Gool,et al.  Content-Based Image Retrieval Based on Local Affinely Invariant Regions , 1999, VISUAL.

[21]  Louis Vuurpijl,et al.  Using Pen-Based Outlines for Object-Based Annotation and Image-Based Queries , 1999, VISUAL.

[22]  Cordelia Schmid,et al.  A Comparison of Affine Region Detectors , 2005, International Journal of Computer Vision.

[23]  David Nistér,et al.  An efficient solution to the five-point relative pose problem , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[24]  Dirk Haehnel,et al.  Are GSM Phones THE Solution for Localization? , 2006, WMCSA.

[25]  Paramvir Bahl,et al.  RADAR: an in-building RF-based user location and tracking system , 2000, Proceedings IEEE INFOCOM 2000. Conference on Computer Communications. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies (Cat. No.00CH37064).

[26]  Harald Haas,et al.  Pedestrian Dead Reckoning (PDR) and GPS for Indoor Positioning , 2006 .

[27]  Piotr Ptasinski,et al.  A method for dead reckoning parameter correction in pedestrian navigation system , 2003, IEEE Trans. Instrum. Meas..

[28]  John Krumm,et al.  Location-aware computing comes of age , 2004, Computer.

[29]  Ales Leonardis,et al.  High-Dimensional Feature Matching: Employing the Concept of Meaningful Nearest Neighbors , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[30]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .