Swarun Kumar
Dina Katabi
ABSTRACT
Recent years have seen the advent of new RF-localization systems that demonstrate tens of centimeters of accuracy. However, such systems require either deployment of new infrastructure, or extensive fingerprinting of the environment through training or crowdsourcing, impeding their wide-scale adoption.
We present Ubicarse, an accurate indoor localization system for commodity mobile devices, with no specialized infrastructure or fingerprinting. Ubicarse enables handheld devices to emulate large antenna arrays using a new formulation of Synthetic Aperture Radar (SAR). Past work on SAR requires measuring mechanically controlled device movement with millimeter precision, far beyond what commercial accelerometers can provide. In contrast, Ubicarse's core contribution is the ability to perform SAR on handheld devices twisted by their users along unknown paths. Ubicarse is not limited to localizing RF devices; it combines RF localization with stereo-vision algorithms to localize common objects with no RF source attached to them. We implement Ubicarse on a HP SplitX2 tablet and empirically demonstrate a median error of 39 cm in 3-D device localization and 17 cm in object geotagging in complex indoor settings.
- VICON T-Series. http://www.vicon.com. VICON.Google Scholar
- M. Agrawal and K. Konolige. Real-time localization in outdoor environments using stereo vision and inexpensive gps. ICPR. 2006. Google ScholarDigital Library
- M. Azizyan, I. Constandache, and R. Roy Choudhury. Surroundsense: Mobile phone localization via ambience fingerprinting. MobiCom, 2009. Google ScholarDigital Library
- F. Bowman. Introduction to Bessel Functions. Dover Publications, 2012.Google Scholar
- K. Chintalapudi, A. Padmanabha Iyer, and V. N. Padmanabhan. Indoor localization without the pain. MobiCom, 2010. Google ScholarDigital Library
- I. Constandache, R. Roy, and C. I. Rhee. Compacc Using mobile phone compasses and accelerometers for localization. In In INFOCOM, 2010.Google Scholar
- R. Fenby. Limitations on directional patterns of phase-compensated circular arrays. REE, 1965.Google ScholarCross Ref
- B. Ferris et al. WiFi-SLAM using gaussian proces latent variable models. IJCAI, 2007. Google ScholarDigital Library
- P. J. Fitch. Synthetic Aperture Radar. Springer, 1988. Google ScholarDigital Library
- H. Foroosh, J. Zerubia, and M. Berthod. Extensio of phase correlation to subpixel registration. Image Processing, IEEE Transactions on, 2002. Google ScholarDigital Library
- J. Georgy et al. Modeling the stochastic drift of a mems-based gyroscope in gyro/odometer/gps integrated navigation. IEEE ITS, 2010. Google ScholarDigital Library
- A. Goswami, L. Ortiz, and S. Das. Wigem: A learning-based approach for indoor localization. CoNEXT, 2011. Google ScholarDigital Library
- D. Halperin, W. Hu, A. Sheth, and D. Wetherall. Tool release: Gathering 802.11n traces with channel state information. ACM SIGCOMM CCR, 2011. Google ScholarDigital Library
- M. Hölzl, R. Neumeier, and G. Ostermayer. Analysis of compass sensor accuracy on several mobile devices in an industrial environment. In EUROCAST. 2013.Google Scholar
- K. Joshi, S. Hong, and S. Katti. PinPoint: Localizing Interfering Radios. NSDI, 2013. Google ScholarDigital Library
- Z. Li et al. Robust statistical methods for wireless localization in sensor networks. In IPSN, 2005. Google ScholarDigital Library
- H. Lim, L.-C. Kung, J. C. Hou, and H. Luo. Zero-configuration indoor localization over 802.11 wireless infrastructure. IEEE Wirel. Netw., 2010. Google ScholarDigital Library
- H. Liu, Y. Gan, J. Yang, S. Sidhom, Y. Wang, Y. Chen, and F. Ye. Push the limit of WiFi based localization for smartphones. Mobicom, 2012. Google ScholarDigital Library
- K. Liu et al. Guoguo: Enabling fine-grained indoor localization via smartphone. MobiSys, 2013. Google ScholarDigital Library
- Microsoft. Kinect. http://www.kinect.com.Google Scholar
- R. Nandakumar et al. Centaur: Locating devices in an office environment. Mobicom, 2012. Google ScholarDigital Library
- M. Niessner, A. Dai, and M. Fisher. Combining Inertial Navigation and ICP for Real-time 3D Surface Reconstruction. Eurographics, 2014.Google Scholar
- H. Nyqvist et al. A high-performance tracking system based on camera and IMU. In FUSION, 2013.Google Scholar
- S. J. Orfanidis. Electromagnetic waves and antennas. http://www.ece.rutgers.edu/~orfanidi/ewa/.Google Scholar
- H. Rahul et al. Jmb: Scaling wireless capacity with user demand. In SIGCOMM, 2012. Google ScholarDigital Library
- A. Rai, K. K. Chintalapudi, V. N. Padmanabhan, and R. Sen. Zee: Zero-effort crowdsourcing for indoor localization. Mobicom, 2012. Google ScholarDigital Library
- S. Sen, B. Radunovic, R. R. Choudhury, and T. Minka. You are facing the mona lisa: Spot localization using phy layer information. MobiSys, 2012. Google ScholarDigital Library
- S. P. Tarzia, P. A. Dinda, R. P. Dick, and G. Memik. Indoor localization without infrastructure using the acoustic background spectrum. MobiSys, 2011. Google ScholarDigital Library
- B. Triggs et al. Bundle adjustment - a moder synthesis. In Vision Algorithms Theory/Practice, 2000. Google ScholarDigital Library
- D. Tse and P. Vishwanath. Fundamentals of Wireless Communications. Cambridge University Press, 2005. Google ScholarDigital Library
- H. Wang, S. Sen, A. Elgohary, M. Farid, M. Youssef, and R. R. Choudhury. No need to war-drive: Unsupervised indoor localization. MobiSys, 2012. Google ScholarDigital Library
- J. Wang, F. Adib, R. Knepper, D. Katabi, and D. Rus. RF-compass: Robot object manipulation using rfids. MobiCom, 2013. Google ScholarDigital Library
- J. Wang and D. Katabi. Dude, where's my card?: RFID positioning that works with multipath and non-line of sight. SIGCOMM, 2013. Google ScholarDigital Library
- WiGle. Database. https://wigle.net.Google Scholar
- C. Wu. VisualSFM : A Visual Structure from Motion System. http://ccwu.me/vsfm/.Google Scholar
- C. Wu, Z. Yang, Y. Liu, and W. Xi. Wireless indoor localization without site survey. In INFOCOM, 2012.Google Scholar
- J. Xiong and K. Jamieson. Arraytrack: A fine-grained indoor location system. NSDI, 2013. Google ScholarDigital Library
- Z. Yang, C. Wu, and Y. Liu. Locating in fingerprint space: Wireless indoor localization with little human intervention. Mobicom, 2012. Google ScholarDigital Library
- M. Youssef and A. Agrawala. The Horus WLAN location determination system. MobiSys, 2005. Google ScholarDigital Library
Index Terms
- Accurate indoor localization with zero start-up cost
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