Inertial Odometry on Handheld Smartphones Arno Solin 1 es 1 Esa - - PowerPoint PPT Presentation

Inertial Odometry

• n Handheld Smartphones

Arno Solin1 Santiago Cort´ es1 Esa Rahtu2 Juho Kannala1

1Aalto University 2Tampere University of Technology

21st International Conference on Information Fusion

July 12, 2018

Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 2/20

Introduction

◮ Phones have accelerometers and

gyroscopes

◮ Should in theory enable inertial

navigation

◮ Cheap and small sensors ◮ Low quality data

inertial navigation not possible We demonstrate that it can be done

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Inertial navigation: How it should work

◮ Velocity is the integral of acceleration. ◮ Position is the integral of velocity. ◮ We can observe acceleration and

angular velocity in the mobile phone.

Inertial navigation

✦ Velocity is the integral of acceleration. ✦ Position is the integral of velocity. ✦ We can observe acceleration and angular velocity in the mobile phone.

Position Velocity Acceleration

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Inertial navigation: Why it does not work

◮ All inertial navigation systems suffer from integration drift. ◮ Small errors in the measurement of acceleration and

angular velocity...

◮ Progressively larger errors in velocity... ◮ Even greater errors in position. ◮ The dominating component in acceleration is gravity. ◮ Even slight error in orientation makes the gravity ‘leak’. ◮ The sequential nature of the problem makes the errors

accumulate.

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Inertial navigation: How to make it work

◮ Input: accelerometer data ak and gyroscope data ωk. ◮ Accelerometer and gyroscope biases part of the state:

˜ ak = Ta

k ak − ba k

˜ ωk = ωk − bω

k ◮ Dynamical model:

  pk vk qk   =   pk−1 + vk−1∆tk vk−1 + [qk(˜ ak + εa

k)q⋆ k − g]∆tk

Ω[(˜ ωk + εω

k )∆tk]qk−1

  for position pk, velocity vk, and orientations qk at time tk.

◮ Inference by an Extended Kalman filter / smoother

Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 6/20

Inertial navigation: How to make it work

◮ Additional constraints (observations) are required ◮ This framework can use

◮ Zero-velocity updates (ZUPTs) ◮ Position fixes ◮ Loop-closures ◮ Barometric air pressure for relative height

◮ A pseudo-measurement keeping the velocity

component from exploding

◮ Sensor timing info ◮ A matter of learning the biases

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How does this compare to previous approaches?

On mobile devices PDR typically based on:

◮ Movement detection ◮ Step and heading systems ◮ Visual features ◮ All of these are limited in some way

Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 8/20

Example studies

◮ Equipment used:

• Off-the-shelf iPhone 6

◮ Sensors:

• Built-in gyroscope and accelerometer
• Sampling rate: 100 Hz

◮ Computations:

• Off-line

(runnable on device hardware)

Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 9/20

Example: Conventional PDR

Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 10/20

Example: Conventional PDR

Position fix Bag Pocket Position fix 2 4 6 z-displacement (m) −2 2 Velocity (m/s) x y z 10 20 30 40 50 60 70 80 90 100 110 120 10 20 Time (seconds) Orientation (rad)

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Example: Conventional PDR

−8 −4 4 −8 −6 −4 −2 2 Floor level 3 Floor level 2 Floor level 1 Elevator ride Stairs Horizontal (y) displacement (meters) Vertical (z) displacement (meters)

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Example: General dead-reckoning

iPhone 6 Baby Freely turning front wheels

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Example: General dead-reckoning

The video is available on YouTube: https://youtu.be/L-E9fNsrvII

Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 14/20

Example: Inertial measurements

The video is available on YouTube: https://youtu.be/L-E9fNsrvII

Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 15/20

Example: Inertial measurements

7.36 m 8.35 m 7.42 m 8.33 m

(a) Measurement #1

7 . 3 6 m 8.41 m 7 . 3 9 m 8.47 m

(b) Measurement #2

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Recap

◮ Inertial navigation on a

smartphone is possible

◮ The key is learning the sensor

transformations (biases)

◮ Not possible without

measurements (consraints)

• ZUPTs
• pseudo-speed

◮ Future work towards relaxing

these further

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◮ Homepage:

http://arno.solin.fi

◮ Twitter:

@arnosolin