2.5D location in Placelab (Formerly Buddy List with Groups) Harlan Hile Alan Liu

Basic concept Application: Want to determine floor as well as location, so other applications can be built on this Platform: Testing on tablets Extending particle filter to estimate floor  Grid is still only 2D Also tried other methods besides particles

Basic Scenario Storyboard: A user is trying to locate someone on their buddy list, enhanced with Placelab. In addition to finding their buddy’s x,y location, they can also see the floor Currently only x,y location is given by Placelab. This is suitable for large scale/outdoor applications, but insufficient for building scale applications

Architecture Application model: A new tracker plug-in that fits inside Placelab; applications may then use for richer data Major pieces:  Beacon database includes extra information (floor)  Particles include extra variable Motion model updated to change floor variable Sensor model to include floor attenuation

Retrospective Updated beacon database format, new classes for tracker, particle, sensor and motion model extending the old one, and made a mapping application  Hard to find the right classes to extend  Hard to find good GPS coordinates for building Floor estimation is reasonably accurate Next steps? Improve models? Full 3D? Next hard problem—improving accuracy to building scale

Evaluation– floor esimation Mode right 86% of time, particles 59%

Evaluation – location estimation Average error: particles 31m centroid of floor 16m particle of floor 19m

Related Work Papers to which this project is most closely related?  Placelab, RADAR What published ideas does it draw on?  Particle Filters Do you think this is publishable work? Why?  Maybe, as an example extension for Placelab indoors  Should have better accuracy (floor and location) Can surely tune particles to do better

Trying to improve accuracy Learn the sensor model from the data  Histogram based, like the intel version  Assume all distances planar  Or only use data from same-floor readings  Or also bin based on floor  Would like to find APs that are outliers, treat them differently Have sensor model influenced by mode Make motion model more fit to office  Particles may sit still for a long time

Learned Sensor Model -- floors 59% 86% 58% 68%

Learned Sensor Model -- accuracy Average error: particles 31m centroid of floor 16m particle of floor 19m Also added new motion model, minor accuracy improvement particle/mode 13.5m all floor model 16m binned floor model 18m