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

Inertial Odometry on Handheld Smartphones Arno Solin 1 es 1 Esa Rahtu 2 Juho Kannala 1 Santiago Cort´ 1 Aalto University 2 Tampere University of Technology 21st International Conference on Information Fusion July 12, 2018

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

Inertial navigation: How it should work Inertial navigation ◮ Velocity is the integral of acceleration. ✦ Velocity is the integral of acceleration. ◮ Position is the integral of velocity. ✦ Position is the integral of velocity. ◮ We can observe acceleration and angular velocity in the mobile phone. ✦ We can observe acceleration and angular velocity in the mobile phone. Position Velocity Acceleration Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 3/20

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. Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 4/20

Inertial navigation: How to make it work ◮ Input: accelerometer data a k and gyroscope data ω k . ◮ Accelerometer and gyroscope biases part of the state: a k = T a k a k − b a ˜ ω k = ω k − b ω ˜ k k ◮ Dynamical model:  p k   p k − 1 + v k − 1 ∆ t k   = a k + ε a v k − 1 + [ q k (˜ v k k ) q ⋆ k − g ]∆ t k    q k k )∆ t k ] q k − 1 Ω [(˜ ω k + ε ω for position p k , velocity v k , and orientations q k at time t k . ◮ Inference by an Extended Kalman filter / smoother Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 5/20

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

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 7/20

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 8/20

Example: Conventional PDR Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 9/20

Example: Conventional PDR z -displacement (m) 6 4 Bag Pocket 2 Position fix Position fix 0 Velocity (m/s) 2 0 x y z − 2 Orientation (rad) 10 20 0 0 10 20 30 40 50 60 70 80 90 100 110 120 Time (seconds) Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 10/20

Example: Conventional PDR 2 Floor level 3 Vertical ( z ) displacement (meters) 0 Stairs Elevator ride − 2 Floor level 2 − 4 − 6 Floor level 1 − 8 − 8 − 4 0 4 Horizontal ( y ) displacement (meters) Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 11/20

Example: General dead-reckoning iPhone 6 Baby Freely turning front wheels Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 12/20

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 13/20

Example: Inertial measurements The video is available on YouTube: https://youtu.be/L-E9fNsrvII Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 14/20

Example: Inertial measurements 7 7.36 m 3 . 6 m 8.35 m 8.41 m 8.47 m 8.33 m 7.42 m m 3 9 . 7 (a) Measurement #1 (b) Measurement #2 Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 15/20

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 Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 16/20

◮ Homepage: http://arno.solin.fi ◮ Twitter: @arnosolin Inertial odometry on handheld smartphones Solin, Cort´ es, Rahtu, Kannala 17/20