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Inertial Measurement Units II Inertial Measurement Units II Gordon Wetzstein Stanford University EE 267 Virtual Reality Lecture 10 stanford.edu/class/ee267/ Polynesian Migration wikipedia Lecture Overview • short review of coordinate systems, tracking in flatland, and accelerometer-only tracking • rotations: Euler angles, axis & angle, gimbal lock • rotations with quaternions • 6-DOF IMU sensor fusion with quaternions • primary goal: track orientation of head or device • inertial sensors required pitch, yaw, and roll to be determined oculus.com from lecture 2: vertex in clip space vertex vclip = M proj ⋅ M view ⋅ M model ⋅v oculus.com from lecture 2: vertex in clip space vertex vclip = M proj ⋅ M view ⋅ M model ⋅v projection matrix view matrix model matrix rotation translation M view = R⋅T (−eye) oculus.com Euler angles θ y rotation translation M R T eye θ x view = ⋅ (− ) θz R = Rz (−θz )⋅ Rx (−θ x )⋅ Ry (−θ y ) roll pitch yaw oculus.com Euler angles θ y 2 important coordinate systems: body/sensor world/inertial frame frame M R T eye θ x view = ⋅ (− ) θz R = Rz (−θz )⋅ Rx (−θ x )⋅ Ry (−θ y ) roll pitch yaw oculus.com Gyro Integration aka Dead Reckoning • from gyro measurements to orientation – use Taylor expansion have: angle at have: last time step time step ∂ θ (t + Δt) ≈ θ (t) + θ (t)Δt + ε, ε ∼ O Δt 2 ∂t ( ) want: angle at approximation error! current time step = ω have: gyro measurement (angular velocity) Orientation Tracking in Flatland • problem: track 1 angle in 2D space • sensors: 1 gyro, 2-axis accelerometer VRduino • sensor fusion with complementary IMU filter, i.e. linear interpolation: (t) (t−1) θ = α θ +ω! Δt + (1−α )atan2 a!x ,a!y ( ) ( ) • no drift, no noise! Tilt from Accelerometer • assuming acceleration points up (i.e. no external forces), we can compute the tilt (i.e. pitch and roll) from a 3-axis accelerometer ⎛ 0 ⎞ ⎛ 0 ⎞ a! ⎜ ⎟ ⎜ ⎟ aˆ = = R 1 = Rz (−θz )⋅ Rx (−θ x )⋅ Ry −θ y 1 a! ⎜ ⎟ ( )⎜ ⎟ ⎝ 0 ⎠ ⎝ 0 ⎠ ⎛ ⎞ 2 2 − cos(−θ x )sin(−θz ) θ = −atan2 aˆ ,sign aˆ ⋅ aˆ + aˆ ⎜ ⎟ x ( z ( y ) x y ) = ⎜ cos(−θ x )cos(−θz ) ⎟ ⎜ ⎟ θ = −atan2 −aˆ ,aˆ both in rad sin z ( x y ) ⎝⎜ (−θ x ) ⎠⎟ Euler Angles and Gimbal Lock • so far we have represented head rotations with Euler angles: 3 rotation angles around the axis applied in a specific sequence • problematic when interpolating between rotations in keyframes (in computer animation) or integration à singularities Gimbal Lock The Guerrilla CG Project, The Euler (gimbal lock) Explained – see: https://www.youtube.com/watch?v=zc8b2Jo7mno Rotations with Axis-Angle Representation and Quaternions Rotations with Axis and Angle Representation • solution to gimbal lock: use axis and angle representation for rotation! • simultaneous rotation around a normalized vector v by angle θ • no “order” of rotation, all at once around that vector Quaternions • think about quaternions as an extension of complex numbers to having 3 (different) imaginary numbers or fundamental quaternion units i,j,k q = qw + iqx + jqy + kqz ij = − ji = k i ≠ j ≠ k ki = −ik = j 2 2 2 i = j = k = ijk = −1 jk = −kj = i Quaternions • think about quaternions as an extension of complex numbers to having 3 (different) imaginary numbers or fundamental quaternion units i,j,k q = qw + iqx + jqy + kqz • quaternion algebra is well-defined and will give us a powerful tool to work with rotations in axis-angle representation in practice Quaternions • axis-angle to quaternion (need normalized axis v) ⎛ θ ⎞ ⎛ θ ⎞ ⎛ θ ⎞ ⎛ θ ⎞ q(θ,v) = cos + i vx sin + j vy sin + k vz sin ⎝⎜ 2⎠⎟ ⎝⎜ 2⎠⎟ ⎝⎜ 2⎠⎟ ⎝⎜ 2⎠⎟ !#"#$ !#"#$ !#"#$ !#"#$ qw qx qy qz Quaternions • axis-angle to quaternion (need normalized axis v) ⎛ θ ⎞ ⎛ θ ⎞ ⎛ θ ⎞ ⎛ θ ⎞ q(θ,v) = cos + i vx sin + j vy sin + k vz sin ⎝⎜ 2⎠⎟ ⎝⎜ 2⎠⎟ ⎝⎜ 2⎠⎟ ⎝⎜ 2⎠⎟ !#"#$ !#"#$ !#"#$ !#"#$ q q q q w x y z • valid rotation quaternions have unit length 2 2 2 2 q = qw + qx + qy + qz = 1 Two Types of Quaternions • vector quaternions represent 3D points or vectors u=(ux,uy,uz) can have arbitrary length qu = 0 + iux + j uy + k uz • valid rotation quaternions have unit length 2 2 2 2 q = qw + qx + qy + qz = 1 Quaternion Algebra • quaternion addition: q + p = (qw + pw ) + i(qx + px ) + j(qy + py ) + k(qz + pz ) • quaternion multiplication: qp = (qw + iqx + jqy + kqz )( pw + ipx + jpy + kpz ) = (qw pw − qx px − qy py − qz pz ) + i(qw px + qx pw + qy pz − qz py ) + j(qw py − qx pz + qy pw + qz px ) + k(qw pz + qx py − qy px + qz pw ) + Quaternion Algebra * • quaternion conjugate: q = qw − iqx − jqy − kqz q* • quaternion inverse: q−1 = q 2 −1 • rotation of vector quaternion q u by q : q'u = qquq −1 • inverse rotation: qu = q q'u q • successive rotations by q then q : −1 −1 1 2 q'u = q2 q1 qu q1 q2 Quaternion Algebra • detailed derivations and reference of general quaternion algebra and rotations with quaternions in course notes • please read course notes for more details! Quaternion-based 6-DOF Orientation Tracking Quaternion-based Orientation Tracking 1. 3-axis gyro integration 2. computing the tilt correction quaternion 3. applying a complementary filter Gyro Integration with Quaternions • start with initial quaternion: q(0) = 1+ i0 + j0 + k0 • convert 3-axis gyro measurements ω ! = ω ! , ω ! , ω ! to ( x y z ) instantaneous rotation quaternion as ⎛ ω! ⎞ avoid division by 0! q = q Δt ω! , Δ ⎝⎜ ω! ⎠⎟ angle rotation axis • integrate as (t+Δt) (t) qω = q qΔ Gyro Integration with Quaternions (t+Δt) • integrated gyro rotation quaternion q ω represents rotation from body to world frame, i.e. 1 (world) (t+Δt) (body) (t+Δt)− qu = qω qu qω t • last estimate q ( ) is either from gyro-only (for dead reckoning) or from last complementary filter t t t • integrate as ( +Δ ) ( ) qω = q qΔ Tilt Correction with Quaternions • assume accelerometer measures gravity vector in body (sensor) coordinates a! = a!x ,a!y ,a!z ( ) • transform vector quaternion of a ! into current estimation of world space as 1 (world) (t+Δt) (body) (t+Δt)− qa = qω qa qω q(body) = 0 + ia! + ja! + ka! a x y z Tilt Correction with Quaternions • assume accelerometer measures gravity vector in body (sensor) coordinates a! = a!x ,a!y ,a!z ( ) • transform vector quaternion of a ! into current estimation of world space as 1 (world) (t+Δt) (body) (t+Δt)− qa = qω qa qω • if gyro quaternion is correct, then accelerometer world vector points up, i.e. (world) qa = 0 + i0 + j 9.81+ k0 Tilt Correction with Quaternions • gyro quaternion likely includes drift • accelerometer measurements are noisy and also include forces other than gravity, so it’s unlikely that accelerometer world vector actually points up • if gyro quaternion is correct, then accelerometer world vector points up, i.e. (world) qa = 0 + i0 + j 9.81+ k0 Tilt Correction with Quaternions (world) solution: compute tilt correction quaternion that would rotate q a into up direction (world) how? get normalized vector part of vector quaternion qa ⎛ (world) (world) (world) ⎞ qa qa qa v = ⎜ x , y , z ⎟ ⎜ (world) (world) (world) ⎟ ⎝ qa qa qa ⎠ Tilt Correction with Quaternions (world) solution: compute tilt correction quaternion that would rotate q a into up direction ⎛ n ⎞ qt = q φ, ⎝⎜ n ⎠⎟ ⎛ v ⎞ x ⎛ 0 ⎞ ⎜ ⎟ ⎜ ⎟ −1 vy i 1 = cos(φ) ⇒ φ = cos vy ⎜ ⎟ ⎜ ⎟ ( ) ⎜ ⎟ 0 vz ⎝ ⎠ ⎝ ⎠ ⎛ v ⎞ ⎛ ⎞ x ⎛ 0 ⎞ −vz ⎜ ⎟ ⎜ ⎟ n = vy × ⎜ 1 ⎟ = 0 ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎜ ⎟ ⎝ 0 ⎠ ⎜ v ⎟ ⎝ vz ⎠ ⎝ x ⎠ Complementary Filter with Quaternions • complementary filter: rotate into gyro world space first, then rotate “a bit” into the direction of the tilt correction quaternion (t+Δt) ⎛ n ⎞ (t+Δt) qc = q (1−α )φ, qω 0 ≤ α ≤ 1 ⎝⎜ n ⎠⎟ 1 (world) (t+Δt) (body) (t+Δt)− • rotation of any vector quaternion is then qu = qc qu qc Integration into Graphics Pipeline (t+Δt) • compute q c via quaternion complementary filter first • stream from microcontroller to PC (t+Δt) • convert to 4x4 rotation matrix (see course notes) qc ⇒ Rc −1 • set view matrix to M v i e w = R c to rotate the world in front of the virtual camera Head and Neck Model y y θ x θz lh ln IMU ln −z x pitch around base of neck! roll around base of neck! Head and Neck Model • why? there is not always positional tracking! this gives some motion parallax • can extend to torso, and using other kinematic constraints • integrate into pipeline as M view = T (0,−ln ,−lh )⋅ R⋅T (0,ln ,lh )⋅T (−eye) Must read: course notes on IMUs!.
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