Did You See Bob?: Human Localization using Mobile Phones

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

2. 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?”

3. System Overview • Walking trail: <displacement, direction, time> • Unique audio tones • Assimilated global view • Routes = sequence of < step i, θi>

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

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

6. Location & Trail Errors Diffusion Drift 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

7. 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)

8. 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

9. 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

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

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

12. 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

13. 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

14. 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?