APT: Accurate Outdoor Pedestrian Tracking with Smartphones

1. APT: Accurate Outdoor Pedestrian Tracking with Smartphones TsungYun 20130401

2. Outline • Introduction • Preliminary Experiment • System and Mechanism • Evaluation • Conclusion

3. Introduction • Motivation • Want to build a system to assist the blind people with smartphones by providing accurate location information • GPS measurements show error up to 15 meters in a clear-sky-view environment

4. Introduction • Observations • Pedestrians have regular movements patterns • Although GPS is unsatisfactory, it works well in distinguishing between distant routes • Can easily generate augmented maps on a smartphone • Dead-Reckoning algorithm • Map-Matching algorithm

5. Introduction • Dead-Reckoning algorithm • Accelerometer: walking step • Gyroscope: walking direction • Consume much less energy than GPS • Map-Matching algorithm • Match a walking trace to a route on the map • Challenges • Placement of the smartphone • Error-tolerant

6. Preliminary Experiment • Limitation of GPS system • GPS system achieved error up to 15 meter • GPS readings cannot be improved by itself solely • First issue • If the GPS coordinate stabilizes, then it will not change for at least several hours • staying in one place longer does not help improve GPS accuracy

7. Preliminary Experiment • Collect 15-20 GPS coordinates at three locations at seven different days • Clear view of the sky • Do not mention how far between these locations

8. Preliminary Experiment • Results show that • GPS readings at the same location can differ up to 15 meters • hard to find any obvious temporal or spatial correlation

9. Preliminary Experiment • Walks along a route 5 times • a large portion of this route is covered by trees • Result shows • the error can still be more than 20 meters • no obvious error pattern

10. Preliminary Experiment • Conclusion • We find that it is unlikely to improve localization accuracy based solely on GPS • In this work, the use of GPS is limited to help reduce route ambiguity in the Map-Matching algorithm

11. Mechanism

12. Mechanism I • Dead-Reckoning – estimating distances • taking the double integral of acceleration results in large error • a common approach is to count the number of walking steps and then multiply it by the stride length • By finding the recurring patterns of accelerometer readings

13. Mechanism I • Different placement of the phone has a large impact on the accuracy of each step counter • 6 recurring patterns • 3 recurring patterns

14. Mechanism I • No matter how the phone is placed, we find that acceleration always shows some recurring patterns • define an up-down pattern as a step • A pattern ‘10’ or ‘1 ∧ 0’ is defined as a step

15. Mechanism I • Using acceleration magnitude, instead of acceleration in a certain direction, can tolerate different ways pedestrians carry the phone • Step length can be measured or trained in advance

16. Mechanism I • Dead-Reckoning – estimating direction • two Cartesian frame of reference • xyz axes V.S. XYZ axes • We can obtain • x y z data • We need • Zdata

17. Mechanism I • straight line -> 90° left turn -> straight line • angular displacement around any axis remains roughly the same before/after the turn

18. Mechanism I • straight line -> 90° left turn -> straight line • acceleration does not fluctuate much before/after the turn, but is quite unusual during the turn

19. Mechanism I • angular displacement around Z-axis • α, β, γ are the angular displacements around x, y, z axis • µx, µy, µzare the acceleration readings in x, y, z direction • the average acceleration during a straight walk should approximate gravity • Z-axis vector (the gravity) is decomposed into three components ???????

20. Mechanism I • The angular displacement is 91.56◦ in this case • But the error (1.56◦) is inevitable

21. Mechanism II • Map-Matching algorithm

22. Mechanism II • Map-Matching algorithm  Use GPS here  trial-and-error

23. Mechanism II • Map-Matching algorithm • Two position fixes can determine a matching • Basic idea : Trial-and-error • Starting from one position fix, find out all possible routes • use subsequent points in the walk to test and extend these routes

24. Mechanism II • Map-Matching algorithm • Assume “perfect information” • First assume that accelerometer, gyroscope, GPS readings are 100% accurate • Update when • New step • New turn • New GPS reading

25. Mechanism II  Use GPS here  Use MAP here  Reversely check ↑Use GPS here ↑ Use GPS here if multiple routes to reduce ambiguity

26. Mechanism II • Dealing with errors • Initial routes • We enumerate all possible locations of the user on the map by considering GPS error • A new step • An adjacent route segment is possible if walking to it only requires a shallow turn within angular error tolerance

27. Mechanism II • Dealing with errors • A new turn • Find out all route segments that are reachable by a turn within the range: the reported angular displacement plus/minus angular error tolerance • A new GPS coordinate • When a new GPS coordinate is available, check each possible route by verifying whether the new GPS coordinate is within a certain distance: (distance error tolerance plus GPS error)

28. Mechanism II • Map-Matching algorithm • If no possible route exists • the system will restart by requesting a new GPS coordinate • When a step and a turn arrive simultaneously • ignoring the steps during a turn • When the number of possible routes becomes intolerable • request a GPS coordinate

29. Evaluation • Experiment • In each second • 50 accelerometer readings • 50 gyroscope readings • 1 GPS reading ???? Energy ???? • Tolerance setting • Distance error tolerance : 20 m • Angular error tolerance : 30° • Based on experience and haven’t been optimized

30. Evaluation

31. Evaluation • Compare APT algorithm to: • Raw GPS coordinates tracking system • Combine the raw GPS coordinates with the map information • In all three routes, our algorithm have consistently less error • The most complicated route, contains more turns, the error is 0 at most anchor points • The error at non-turn anchor points is at most 5m

32. Evaluation

33. Conclusion • This paper present APT, a system targeting at accurate pedestrian localization • Uses the accelerometer, gyroscope and GPS component of modern smartphones, and integrates them with map information • Can tolerate GPS error and the different ways to hold the smartphone • Achieve better performance than GPS only