Vision-aided L in addition to mark Routing in addition to Localization Characteristic dist. in addition to Infinite paths Vision-aided L in addition to mark Routing in addition to Localization

Vision-aided L in addition to mark Routing in addition to Localization Characteristic dist. in addition to Infinite paths Vision-aided L in addition to mark Routing in addition to Localization www.phwiki.com

Vision-aided L in addition to mark Routing in addition to Localization Characteristic dist. in addition to Infinite paths Vision-aided L in addition to mark Routing in addition to Localization

Barkoff, Rupert, Contributing Editor has reference to this Academic Journal, PHwiki organized this Journal Aaron Ballew Aleks in addition to ar Kuzmanovic C. C. Lee Shiva Srivastava Nikolay Valtchanov Northwestern University, Evanston IL, USA Dept. of Electrical Engineering in addition to Computer Science July 4th 2012 Vision-aided L in addition to mark Routing in addition to Localization Indoor GPS Many facilities publish free floor plans online Hyatt Regency O’hare RF-derived approaches Triangulation Delay, Angle, RSSI RF Signatures Beacons Impulse response

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Use logic What happens in practice A person reports what they see Take advantage of relationships among identifiable features of a room Absolute precision is not as important as a person comprehending where he is Important Definitions Isovist: The visible area from a location’s perspective Vi,j = { }, set of coordinates visible from point (i,j) V’i,j = { }, set of coordinates invisible from point (i,j) Feature: An identifiable l in addition to mark, e.g. cash register, bathroom, elevator Feature Vector: [f1 f2 fh], where fi Î{0,1} fi == 1, fi is visible fi == 0, fi is invisible Region: Subset of coordinates sharing an identical feature vector G B R If feature A ÎVi,j, then point (i,j) ÎVA i.e. if you can see a feature, then that feature can see you. In general, as long as all features fp reported visible, in addition to all fq reported invisible: Location 2h (or 2h-1) locatable regions, as long as h total features in the environment A simple example: 3 features

4 features In general, as long as all features fp reported visible, in addition to all fq reported invisible: Location 2h possible vectors, so locatable regions grow in O(2h) Corollary: Average region size decreases as h increases region = 11 < 2h-1 = 15 Conjecture: For h>3, there is no 2D arrangement of features that generates all 2h-1 locatable regions User error Mistakes happen Type I Error: Report an invisible feature is visible Possible, but rare Example: confusing “stairs” with “escalator.” Type II Error: Report a visible feature is invisible Not only possible, it’s probable Our study revealed ~50% hit-rate on noticing features Assume positive sightings are trustworthy, in addition to negative sightings are completely untrustworthy Sacrifice all info gained from unsighted features All in as long as mation comes from accumulation of positive sightings Avg. located area vs. h features Important result The plausible range of operation (2:5 sightings) per as long as ms in line with the unlimited range of operation

Finding your way Model environment as a network nodes (features) in addition to links (intervisibilities) At each hop, report what you see app recommends a new next hop Only adjacent subset of features are offered at each hop Makes the list much smaller If you don’t sight a feature, the link is down Ineligible as a next hop Hop-by-Hop SPF Routing Measurements of Characteristic Distance in addition to Infinite Paths The more sensitive behavior is infinite paths, not hop count Hop-by-Hop SPF Routing Sharp transition where the graph is connected “almost surely” The threshold corresponds to

Characteristic dist. in addition to Infinite paths Example of agreement between simulated r in addition to om graph in addition to a real graph of a test location The more sensitive behavior is infinite paths, not hop count Simulated R in addition to om Network h = 40, p = 0.25 Real Test Network h = 38, p = 0.283 Large Hotel/Convention Center (with permission) h = 38 features p = .283 edge density 10 volunteers with no prior knowledge Test 1 – sighting features Each subject tested from multiple vantage points Positive sightings rate pv = 0.496, with 90% confidence pv > 0.46 Test 2 – usability Wayfinding task A B Tracked user experience Willingness to use the tool Ability to use the tool Feedback & suggestions Field Study User/App interaction Based on your input to Part I I know where you are More important – I know what you see Knowing what you see, I can tell you to walk over to it Application picks the best “next hop” on the way to the destination Repeat this in a simple way until the user is within L.O.S. of destination

Conclusions More features in the environment gives better location precision, even with the same number of sightings Constraining to 2:5 sightings behaves similarly to unconstrained case, i.e. plausible tracks feasible Number of hops is less important than whether you get there ppv > (ln n)/n is an indication of high success rate Aaron Ballew Aleks in addition to ar Kuzmanovic C. C. Lee Shiva Srivastava Nikolay Valtchanov Northwestern University, Evanston IL, USA Dept. of Electrical Engineering in addition to Computer Science July 4th 2012 Vision-aided L in addition to mark Routing in addition to Localization

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