Unsupervised Indoor Localization No Need to War-Drive

Transcription

1 Unsupervised Indoor Localization No Need to War-Drive Gordon Devoe Department of Computer Science and Engineering University of Connecticut CSE5095: Networked Embedded Systems

2 Introduction - 1 UnLoc: an unsupervised indoor localization scheme.

3 Basic Idea from the Past Track human walking trajectory (called dead reckon) Using accelerometer and electronic compass Estimated path GPS corrected path Steps

4 Dead Reckoning Urban dead-reckoning is an established idea that uses the accelerometer and compass to track a mobile user. Accelerometer counts steps walked. Compass detects direction of steps. Derive displacement of person. Resets when defined GPS coordinates are crossed. <displacement, direction, time>

5 Basic Idea from the Past Successfully used for outdoor localization Use GPS reset points (landmarks) and dead-reckon inbetween Unfortunately, cannot employ the same idea indoors GPS is unavailable

6 Indoor Localization Challenges Requires higher degree of granularity (5m location error outdoors may indicate same street but indoors 5m may mean a different aisle in a grocery store. Precision is attainable with pervasive WiFi systems but it comes with a high-cost. UnLoc: attempts to achieve meter level accuracy with zero calibration. Motion Trajectory Urban Dead Reckoning (accelerometer & compass) Ambience & User Behavior Magnetic fluctuations, Sound, Walking, Turning, Climbing Wifi Use AP s to partition indoor space in to smaller sub-spaces

7 Need Indoor Reset Points (landmarks) At first we could not see any reset opportunities But what if we see through the eyes of sensors?

8 Introduction - 2 Key Idea: Certain locations in an indoor environment present identifiable key signatures on one or more sensing dimensions. Elevator: distinct pattern on smartphone accelerometer. Corridor-corner: may overhear a unique set of WiFi access points. These types of signatures naturally exist and can be envisioned as internal landmarks of a building.

9 Many Many Landmarks

10 Let the Patterns Emerge Of course, not looking for pre-determined patterns Rather, let patterns emerge from sensor readings E.g., through multi-dimensional clustering

11 Landmark Need not be Unique per Building Uniqueness needed only within WiFi Area

12 Seed Landmarks (SLMs) Structures in a building that force users to behave in a particular way. These predictable behaviors can be translated to sensor signatures. Examples: Stairs Elevators (start/stop) Escalator Building entrances (indoors/outdoors) How do we localize when Alice moves between these landmarks?

13 Organic Landmarks (OLMs) Any indoor environment will offer some ambient signatures across one or more seeing directions. Magnetic Domain metals may produce unique fluctuations on magnetometer. WiFi Based overheard WiFi base-stations.

14 Our Hypothesis Indoor environments have adequate landmarks (i.e., reset points) to keep dead-reckoning error below desired thresholds

15 We Have Localization Reset the error whenever you sense a landmark

16 But wait How can you reset location error? You don t know the location of the landmarks Solution intuition: Kalman Filter 1. Use dead reckoning to estimate landmarks 2. Use landmarks to improve dead reckoning 3. Landmark estimation errors independent system converges

17 Estimating Landmark Locations Within 2 man hours, discovered 13 to 18 landmarks per floor, and landmark location error ~1.26m

18 Convergence Does the landmark location converge? No theoretical proof Dead-reckoning error is function of sensor noise (i.i.d) Results from extensive testing suggest convergence

19 System still not functional Compass error indoors derail dead reckoning Solution intuition: 1. Use gyroscopes to understand relative angular rotation 2. Periodically calibrate the gyroscope with the compass

20 Let s Put Them All Together

21 Recursive Algorithm Users motion traces <time, sensor value> Landmarks Dead Reckon using existing landmarks Localization Error Unique Sensor Fingerprint? Find Landmark Location Update Landmark list

22 Evaluation Android Nexus1 and NexusS phones Realtime location tracking on phone Offline optimizations on server Assumptions Phone held in hand Tuned to person s step size (by walking a known distance) Only Android platform (no cross-platform experiments)

23 UnLoc Performance Experimentation on 8000 sq. meters Shopping mall, ECE and CS buildings

24 UnLoc Performance Error peaks and resets on encountering landmarks Error peaks reduces as more landmarks discovered

25 Other Properties of UnLoc Not power hungry Inertial sensors + WiFi + GSM + 3G Robust Unanticipated problems arise systems get derailed Landmark match brings UnLoc to immediate stability Churn in landmarks UnLoc learns new landmarks Forgets ones that change

26 Closing Thoughts UnLoc built on the shoulders of lots of prior work: Use of radio signals indoors from RADAR, Place Labs, Horus and of course FM fingerprinting and PinLoc Sensor fingerprints from many paper in context-awareness The recursive approach from SLAM, dead-reckoning, However, we believe that UnLoc connects these ideas systematically, into a viable system

27 Summary Indoor environments are rich in multi-modal fingerprints Possible to simultaneously localize and map them, even using noisy smartphone sensors UnLoc achieves this: m accuracy - No calibration needed - No infrastructure cost - Low energy

28 UnLoc Demo UnLoc demo

29 References [UNLOC]He Wang, Souvik Sen, Ahmed Elgohary, Moustafa Farid, Moustafa Youssef, and Romit Roy Choudhury. No need to war-drive: unsupervised indoor localization. MobiSys