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The Location Stack: Design and Implementation of Multisensor Location Systems

The Location Stack: Design and Implementation of Multisensor Location Systems. Jeffrey Hightower. Acknowledgements. Gaetano Borriello Dieter Fox Dirk Schulz Barry Brumitt Matthai Philipose. Kelvin Lau Waylon Brunette Seila Kheang Travis Martin Lindsey Irwin. Anthony LaMarca

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The Location Stack: Design and Implementation of Multisensor Location Systems

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  1. The Location Stack:Design and Implementation of Multisensor Location Systems Jeffrey Hightower

  2. Acknowledgements Gaetano Borriello Dieter Fox Dirk Schulz Barry Brumitt Matthai Philipose Kelvin Lau Waylon Brunette Seila Kheang Travis Martin Lindsey Irwin Anthony LaMarca Eugene Shih Daniel Dunham Larry Arnstein

  3. Ad hoc signal strength GPS Cellular E-911 Physical contact Ultrasonic time of flight DC magnetic pulses Laser range-finding Infrared proximity Stereo vision A survey & taxonomy of location technologies [Hightower and Borriello, IEEE Computer, Aug 2001]

  4. Problems Identified • Design abstractions for location-aware applications are poor. • Substantial benefit is lost by not fusing information from multiple location sensor technologies.

  5. Intentions Activities Contextual Fusion Non- Location Context Abstractions Arrangements Fusion Measurements Sensors The Location Stack 5 Principles • There are fundamental measurement techniques. • There are standard ways to combine measurements. • There are standard object relationship queries. • Applications are concerned with activities. • Uncertainty is important. [Hightower, Brumitt, and Borriello, WMCSA, Jan 2002]

  6. Principle 5: Uncertainty is important. Example: routing phone calls to nearest handset X [Hightower and Borriello, Ubicomp LMUC Workshop, Sep 2001]

  7. Simple infrared badge tracking using particle filters One person wearing an infrared badge and walking around half-height cubicles.

  8. Bel(x) Bel(x) Bel(x) Bel(x) x x x x Particle filters: a quick tutorial

  9. Motion models: probability distance Sensor likelihood models: Particle filters: motion and sensors Stochastically move all particles. t+1 t+2 R B

  10. Supported Sensor Technologies • VersusTech commercial infrared badge proximity system • RF Proximity tagging using the Berkeley motes • SICK LMS-200 180º infrared laser range finders • MIT Cricket ultrasound time-of-flight range beacons • GPS • 802.11b WiFi signal strength analysis • Passive RFId sensors • Cellular telephone E-OTD, planned

  11. Multi-sensor example Infrared badges Ultrasound badge 5 minute trace log of six people walking around randomly

  12. A 1 3 5 B Track confusion 2 4 6 People Tracking with Anonymous and ID Sensors • Anonymous sensors provide accurate location but no identity. • ID sensors object identity but coarse location. • Idea: Sensor fusion to get benefits of both. [Fox, Hightower, and Schulz, Submitted to IJCAI, 2003]

  13. Multi-sensor example

  14. Future Work • Further evaluate the Location Stack through use in additional research and commercial applications. • Collaborate with the machine learning community to work on contextual fusion and activity inference. • Use mobile robots to learn and update models of the environment and the sensors.

  15. Conclusion Relying on a single sensor technology to support all location-aware applications is inappropriate. Instead, the Location Stack provides: • The ability to fuse measurements from many technologies including both anonymous and id-sensors while still preserving sensor uncertainty. • Design abstractions enabling system evolution as new sensor technologies are created. • A common vocabulary to partition the work and research problems appropriately.

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