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Sherlock: Automating Object Location for Humans

Solving the challenge of locating countless objects by integrating RFID technology with practical room-level infrastructure, enhancing search efficiency and reducing manual effort.

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Sherlock: Automating Object Location for Humans

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  1. Sherlock: Automatically Locating Objects for Humans • Aditya Nemmaluri, Mark D. Corner, Prashant Shenoy • Department of Computer Science • UMass Amherst

  2. Can’t Find Your Keys? • People own uncountable objects (1000s?) • Humans don’t posses DB indexing abilities • Lose, lend, misplace, waste time, rebuy, ... • A grand challenge for pervasive computing

  3. Wouldn’t it be Nice? Index, Search, and Locate Anything!

  4. RFID Changes Everything • Non-computers become computers • For dimes, pennies, or less • no batteries = scalability • Affix tags to every inanimate object • Clothes, books, tools, doors, food, trash...

  5. Challenges • Localization: the finer the better • User interfaces: augmented reality • Search: temporal and physical data • Security and privacy

  6. Sherlock • Infrastructure based, steerable antennas • Combine with PTZ cameras • Localize objects to an small area • Rely on humans to do the rest • Practical demonstration in a realistic setting • Search and display results

  7. Sherlock Architecture

  8. RFID Endpoint • RFID reader equipped w/steerable antenna • Can identify each passive tag within view • Can’t localize them directly • Localization depends on (not)seeing tag • Antenna has limited beamwidth/range • Sherlock steers antenna intelligently

  9. Localization-Pan

  10. Localization-Zoom

  11. Idealized Localization Can locate tag to narrow (10 degree sliver)

  12. Does This Work? • Set up 30 tags in a near-ideal setting • 60-70 degree antenna beam width (spec) • Expect to see 60-70 degree tag beam width • Expect low error rates • tag is actually in that narrow 10 degrees

  13. Ideal Results

  14. Realistic Setting • 100 Tags in a one person office • books, doors, coffee mugs, staplers... • metal cabinets, desks, windows, walls...

  15. Realistic Results

  16. Reflections/Occlusions Occlusions Reflections

  17. Conservative Correction Add 30-45 degrees depending on measured beamwidth Yields zero error rate 10 degree sliver becomes 70-100 degrees

  18. Multiple Antennas • Fuse 3D area from multiple antennas • Chances are one gets a good view of tag • Use a 3D intersection algorithm

  19. Scan Strategies • Localization takes time (lots of fine steps) • Delays detection of new or stale objects • Coarse, Fine, Localize: see paper for details

  20. Implementation • Mechanically steerable antenna • substitute for electronically steerable • Two antennas (range: ~3m) • ThingMagic Mercury5 Reader • Alien RFID tags 98x12mm 76x76mm • libGTS graphics library for 3D Intersections

  21. Steerable Antenna PTZ Base as stand in for electronic steering

  22. Evaluation • Same office environment as before • Can it localize objects quickly? • Can it localize to a reasonable volume?

  23. Office Environment

  24. Latency

  25. Single Antenna Useable localization Half of objects are difficult to localize

  26. Two Antennas Many difficult localizations solved with second antenna

  27. Visualization • For each localization take snap shot of area • Project volume onto 2D photo • Works if camera has view of object

  28. Web Interface

  29. Related Work • RFID Localization (Hähnel et. al) • SLAM robotics problem • Ferret (Liu et. al) • mobile reader • RFID Radar • TTF technology, precise timing

  30. Sherlock • Practical room-level object indexing system • Iterative and robust localization algorithm • Visualization and search system

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