APT: Accurate Outdoor Pedestrian Tracking with Smartphones TsungYun

Outline Introduction Preliminary Experiment System and Mechanism Evaluation Conclusion 2

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 3

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 4

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 5

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 6

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

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 8

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 9

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 10

Mechanism 11

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 12

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

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 14

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 15

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 Z data 16

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

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 18

Mechanism I angular displacement around Z-axis – α, β, γ are the angular displacements around x, y, z axis – µ x, µ y, µz are 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 ??????? 19

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

Mechanism II Map-Matching algorithm 21

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

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 23

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 24

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

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 26

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) 27

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 28

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 29

Evaluation 30

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 31

Evaluation 32

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 33