Did You See Bob?: Human Localization using Mobile Phones Constandache, et. al. Presentation by: Akie Hashimoto, Ashley Chou

Introduction & Motivation  Various research in all aspects of localization technology  Tradeoff between energy & location accuracy  Indoor localization techniques  Logical location identification  Escort exands the notion of localization in the social context  In large public areas, navigation without precise knowledge of a person’s location can be non-trivial  Large & crowded  Unfamiliar location  “In a human populated public place, can we develop an electronic system that can localize and route a person A to a specified person B?”

System Overview  Walking trail:  Unique audio tones  Assimilated global view  Routes = sequence of

Design Challenges Noisy Sensors  Location & Trail Errors  Encounter Detection  Trail Graph Density  Visual Identification 

Noisy Sensors AccelerometerCompass Double Integration vs. Step Count Method  Average bias of 8 degrees  (1) Constant direction state: compensate stable readings w/average bias  (2) Turning state: use compass reported readings

Location & Trail Errors DiffusionDrift Cancellation  Diffuse fresh location information into system to compensate for drift by…  (1) Encounters with the beacon  (2) Encounters with users who passed the beacon recently  Use diffusion information to correct past trails  Correction vector estimates cumulative drift over time  Assuming projected path deviates linearly, can amortize correction vector over time

Encounter Detection  Bluetooth too slow for detecting short lived encounters  Clients & beacon employed unique audio tones  Reliability of tone detection tested in 3 scenarios  Transmitter-receiver distance determined via amplitude cutoff (5m threshold)

Trail Graph Density  Phase 1: For every pair of nodes, closest spatial intersection between them retained; all others eliminated  Phase 2: Graph pruned again to only keep shortest path between users  Efficient

Visual Identification  End-to-end: Identify exactly whom to approach  Opportunistically take pictures of mobile phone’s owner  Generate fingerprint of user’s appearance  Camera-based user identification

Evaluation Testbed  Limitations & Future Work  Related Work  Personal Comments 

Testbed  Accuracy  Using markers to show the errors  Sensors (36.2m)  Beacon (8.5m)  Drift Cancellation (6.1m)

Limitations & Future Work  Not energy efficient: sensors and uploading info to the server  Switch off sensors  More frequent beacons  Wrong direction – educated guess  Hidden shortest path – give option for direct path  Low location accuracy – recompute  Phone orientation affect sensors – currently more research on the compass orientation  Scalability – better or worse

Related Work  Location estimated based on the overheard signals and on the data collected during a calibration. (Beacons and RF)  Using AP and its signal strength  Using GPS, Wifi and walking pattern to figure out the location.  SLAM robot collecting beacons and landmarks

Personal Comments  Encounter can cause more errors  Closer to the beacon does not always correlate to better resolution  Encounter itself has maximum of 5m error  Black spots  Some inside location has no GPS or WiFi. Beacon must cover all area.  Second floor?  This paper did not address the possibility of escorting one to another floor. Are stairs, escalators, and elevators still a possibility?