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APT: Accurate Outdoor Pedestrian Tracking with Smartphones

TsungYun 20130401

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Outline

• Introduction• Preliminary Experiment• System and Mechanism • Evaluation• Conclusion

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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

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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

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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

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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

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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

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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

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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

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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

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Mechanism

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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

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Mechanism I

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

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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∧

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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

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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

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Mechanism I

• straight line -> 90° left turn -> straight line– angular displacement around any axis remains

roughly the same before/after the turn

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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

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Mechanism I

• angular displacement around Z-axis

– α, β, γ are the angular displacements around x, y, z axis– µx , µy , 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 ???????

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Mechanism I

• The angular displacement is 91.56◦ in this case– But the error (1.56◦) is inevitable

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Mechanism II

• Map-Matching algorithm

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Mechanism II

• Map-Matching algorithm

Use GPS here trial-and-error

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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

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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

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Mechanism II

Use GPS here

Reversely check

↑ Use GPS here if multiple routes to reduce ambiguity

↑ Use GPS here

Use MAP here

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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

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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)

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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

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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

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Evaluation

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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

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Evaluation

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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