| /* |
| * Copyright (C) 2011 The Android Open Source Project |
| * |
| * Licensed under the Apache License, Version 2.0 (the "License"); |
| * you may not use this file except in compliance with the License. |
| * You may obtain a copy of the License at |
| * |
| * http://www.apache.org/licenses/LICENSE-2.0 |
| * |
| * Unless required by applicable law or agreed to in writing, software |
| * distributed under the License is distributed on an "AS IS" BASIS, |
| * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| * See the License for the specific language governing permissions and |
| * limitations under the License. |
| */ |
| |
| #include <stdio.h> |
| |
| #include <utils/Log.h> |
| |
| #include "Fusion.h" |
| |
| namespace android { |
| |
| // ----------------------------------------------------------------------- |
| |
| /* |
| * gyroVAR gives the measured variance of the gyro's output per |
| * Hz (or variance at 1 Hz). This is an "intrinsic" parameter of the gyro, |
| * which is independent of the sampling frequency. |
| * |
| * The variance of gyro's output at a given sampling period can be |
| * calculated as: |
| * variance(T) = gyroVAR / T |
| * |
| * The variance of the INTEGRATED OUTPUT at a given sampling period can be |
| * calculated as: |
| * variance_integrate_output(T) = gyroVAR * T |
| * |
| */ |
| static const float gyroVAR = 1e-7; // (rad/s)^2 / Hz |
| static const float biasVAR = 1e-8; // (rad/s)^2 / s (guessed) |
| |
| /* |
| * Standard deviations of accelerometer and magnetometer |
| */ |
| static const float accSTDEV = 0.05f; // m/s^2 (measured 0.08 / CDD 0.05) |
| static const float magSTDEV = 0.5f; // uT (measured 0.7 / CDD 0.5) |
| |
| static const float SYMMETRY_TOLERANCE = 1e-10f; |
| |
| /* |
| * Accelerometer updates will not be performed near free fall to avoid |
| * ill-conditioning and div by zeros. |
| * Threshhold: 10% of g, in m/s^2 |
| */ |
| static const float FREE_FALL_THRESHOLD = 0.981f; |
| static const float FREE_FALL_THRESHOLD_SQ = |
| FREE_FALL_THRESHOLD*FREE_FALL_THRESHOLD; |
| |
| /* |
| * The geomagnetic-field should be between 30uT and 60uT. |
| * Fields strengths greater than this likely indicate a local magnetic |
| * disturbance which we do not want to update into the fused frame. |
| */ |
| static const float MAX_VALID_MAGNETIC_FIELD = 100; // uT |
| static const float MAX_VALID_MAGNETIC_FIELD_SQ = |
| MAX_VALID_MAGNETIC_FIELD*MAX_VALID_MAGNETIC_FIELD; |
| |
| /* |
| * Values of the field smaller than this should be ignored in fusion to avoid |
| * ill-conditioning. This state can happen with anomalous local magnetic |
| * disturbances canceling the Earth field. |
| */ |
| static const float MIN_VALID_MAGNETIC_FIELD = 10; // uT |
| static const float MIN_VALID_MAGNETIC_FIELD_SQ = |
| MIN_VALID_MAGNETIC_FIELD*MIN_VALID_MAGNETIC_FIELD; |
| |
| /* |
| * If the cross product of two vectors has magnitude squared less than this, |
| * we reject it as invalid due to alignment of the vectors. |
| * This threshold is used to check for the case where the magnetic field sample |
| * is parallel to the gravity field, which can happen in certain places due |
| * to magnetic field disturbances. |
| */ |
| static const float MIN_VALID_CROSS_PRODUCT_MAG = 1.0e-3; |
| static const float MIN_VALID_CROSS_PRODUCT_MAG_SQ = |
| MIN_VALID_CROSS_PRODUCT_MAG*MIN_VALID_CROSS_PRODUCT_MAG; |
| |
| // ----------------------------------------------------------------------- |
| |
| template <typename TYPE, size_t C, size_t R> |
| static mat<TYPE, R, R> scaleCovariance( |
| const mat<TYPE, C, R>& A, |
| const mat<TYPE, C, C>& P) { |
| // A*P*transpose(A); |
| mat<TYPE, R, R> APAt; |
| for (size_t r=0 ; r<R ; r++) { |
| for (size_t j=r ; j<R ; j++) { |
| double apat(0); |
| for (size_t c=0 ; c<C ; c++) { |
| double v(A[c][r]*P[c][c]*0.5); |
| for (size_t k=c+1 ; k<C ; k++) |
| v += A[k][r] * P[c][k]; |
| apat += 2 * v * A[c][j]; |
| } |
| APAt[j][r] = apat; |
| APAt[r][j] = apat; |
| } |
| } |
| return APAt; |
| } |
| |
| template <typename TYPE, typename OTHER_TYPE> |
| static mat<TYPE, 3, 3> crossMatrix(const vec<TYPE, 3>& p, OTHER_TYPE diag) { |
| mat<TYPE, 3, 3> r; |
| r[0][0] = diag; |
| r[1][1] = diag; |
| r[2][2] = diag; |
| r[0][1] = p.z; |
| r[1][0] =-p.z; |
| r[0][2] =-p.y; |
| r[2][0] = p.y; |
| r[1][2] = p.x; |
| r[2][1] =-p.x; |
| return r; |
| } |
| |
| |
| template<typename TYPE, size_t SIZE> |
| class Covariance { |
| mat<TYPE, SIZE, SIZE> mSumXX; |
| vec<TYPE, SIZE> mSumX; |
| size_t mN; |
| public: |
| Covariance() : mSumXX(0.0f), mSumX(0.0f), mN(0) { } |
| void update(const vec<TYPE, SIZE>& x) { |
| mSumXX += x*transpose(x); |
| mSumX += x; |
| mN++; |
| } |
| mat<TYPE, SIZE, SIZE> operator()() const { |
| const float N = 1.0f / mN; |
| return mSumXX*N - (mSumX*transpose(mSumX))*(N*N); |
| } |
| void reset() { |
| mN = 0; |
| mSumXX = 0; |
| mSumX = 0; |
| } |
| size_t getCount() const { |
| return mN; |
| } |
| }; |
| |
| // ----------------------------------------------------------------------- |
| |
| Fusion::Fusion() { |
| Phi[0][1] = 0; |
| Phi[1][1] = 1; |
| |
| Ba.x = 0; |
| Ba.y = 0; |
| Ba.z = 1; |
| |
| Bm.x = 0; |
| Bm.y = 1; |
| Bm.z = 0; |
| |
| init(); |
| } |
| |
| void Fusion::init() { |
| mInitState = 0; |
| |
| mGyroRate = 0; |
| |
| mCount[0] = 0; |
| mCount[1] = 0; |
| mCount[2] = 0; |
| |
| mData = 0; |
| } |
| |
| void Fusion::initFusion(const vec4_t& q, float dT) |
| { |
| // initial estimate: E{ x(t0) } |
| x0 = q; |
| x1 = 0; |
| |
| // process noise covariance matrix: G.Q.Gt, with |
| // |
| // G = | -1 0 | Q = | q00 q10 | |
| // | 0 1 | | q01 q11 | |
| // |
| // q00 = sv^2.dt + 1/3.su^2.dt^3 |
| // q10 = q01 = 1/2.su^2.dt^2 |
| // q11 = su^2.dt |
| // |
| |
| // variance of integrated output at 1/dT Hz |
| // (random drift) |
| const float q00 = gyroVAR * dT; |
| |
| // variance of drift rate ramp |
| const float q11 = biasVAR * dT; |
| |
| const float u = q11 / dT; |
| const float q10 = 0.5f*u*dT*dT; |
| const float q01 = q10; |
| |
| GQGt[0][0] = q00; // rad^2 |
| GQGt[1][0] = -q10; |
| GQGt[0][1] = -q01; |
| GQGt[1][1] = q11; // (rad/s)^2 |
| |
| // initial covariance: Var{ x(t0) } |
| // TODO: initialize P correctly |
| P = 0; |
| } |
| |
| bool Fusion::hasEstimate() const { |
| return (mInitState == (MAG|ACC|GYRO)); |
| } |
| |
| bool Fusion::checkInitComplete(int what, const vec3_t& d, float dT) { |
| if (hasEstimate()) |
| return true; |
| |
| if (what == ACC) { |
| mData[0] += d * (1/length(d)); |
| mCount[0]++; |
| mInitState |= ACC; |
| } else if (what == MAG) { |
| mData[1] += d * (1/length(d)); |
| mCount[1]++; |
| mInitState |= MAG; |
| } else if (what == GYRO) { |
| mGyroRate = dT; |
| mData[2] += d*dT; |
| mCount[2]++; |
| if (mCount[2] == 64) { |
| // 64 samples is good enough to estimate the gyro drift and |
| // doesn't take too much time. |
| mInitState |= GYRO; |
| } |
| } |
| |
| if (mInitState == (MAG|ACC|GYRO)) { |
| // Average all the values we collected so far |
| mData[0] *= 1.0f/mCount[0]; |
| mData[1] *= 1.0f/mCount[1]; |
| mData[2] *= 1.0f/mCount[2]; |
| |
| // calculate the MRPs from the data collection, this gives us |
| // a rough estimate of our initial state |
| mat33_t R; |
| vec3_t up(mData[0]); |
| vec3_t east(cross_product(mData[1], up)); |
| east *= 1/length(east); |
| vec3_t north(cross_product(up, east)); |
| R << east << north << up; |
| const vec4_t q = matrixToQuat(R); |
| |
| initFusion(q, mGyroRate); |
| } |
| |
| return false; |
| } |
| |
| void Fusion::handleGyro(const vec3_t& w, float dT) { |
| if (!checkInitComplete(GYRO, w, dT)) |
| return; |
| |
| predict(w, dT); |
| } |
| |
| status_t Fusion::handleAcc(const vec3_t& a) { |
| // ignore acceleration data if we're close to free-fall |
| if (length_squared(a) < FREE_FALL_THRESHOLD_SQ) { |
| return BAD_VALUE; |
| } |
| |
| if (!checkInitComplete(ACC, a)) |
| return BAD_VALUE; |
| |
| const float l = 1/length(a); |
| update(a*l, Ba, accSTDEV*l); |
| return NO_ERROR; |
| } |
| |
| status_t Fusion::handleMag(const vec3_t& m) { |
| // the geomagnetic-field should be between 30uT and 60uT |
| // reject if too large to avoid spurious magnetic sources |
| const float magFieldSq = length_squared(m); |
| if (magFieldSq > MAX_VALID_MAGNETIC_FIELD_SQ) { |
| return BAD_VALUE; |
| } else if (magFieldSq < MIN_VALID_MAGNETIC_FIELD_SQ) { |
| // Also reject if too small since we will get ill-defined (zero mag) |
| // cross-products below |
| return BAD_VALUE; |
| } |
| |
| if (!checkInitComplete(MAG, m)) |
| return BAD_VALUE; |
| |
| // Orthogonalize the magnetic field to the gravity field, mapping it into |
| // tangent to Earth. |
| const vec3_t up( getRotationMatrix() * Ba ); |
| const vec3_t east( cross_product(m, up) ); |
| |
| // If the m and up vectors align, the cross product magnitude will |
| // approach 0. |
| // Reject this case as well to avoid div by zero problems and |
| // ill-conditioning below. |
| if (length_squared(east) < MIN_VALID_CROSS_PRODUCT_MAG_SQ) { |
| return BAD_VALUE; |
| } |
| |
| // If we have created an orthogonal magnetic field successfully, |
| // then pass it in as the update. |
| vec3_t north( cross_product(up, east) ); |
| |
| const float l = 1 / length(north); |
| north *= l; |
| |
| update(north, Bm, magSTDEV*l); |
| return NO_ERROR; |
| } |
| |
| void Fusion::checkState() { |
| // P needs to stay positive semidefinite or the fusion diverges. When we |
| // detect divergence, we reset the fusion. |
| // TODO(braun): Instead, find the reason for the divergence and fix it. |
| |
| if (!isPositiveSemidefinite(P[0][0], SYMMETRY_TOLERANCE) || |
| !isPositiveSemidefinite(P[1][1], SYMMETRY_TOLERANCE)) { |
| LOGW("Sensor fusion diverged; resetting state."); |
| P = 0; |
| } |
| } |
| |
| vec4_t Fusion::getAttitude() const { |
| return x0; |
| } |
| |
| vec3_t Fusion::getBias() const { |
| return x1; |
| } |
| |
| mat33_t Fusion::getRotationMatrix() const { |
| return quatToMatrix(x0); |
| } |
| |
| mat34_t Fusion::getF(const vec4_t& q) { |
| mat34_t F; |
| F[0].x = q.w; F[1].x =-q.z; F[2].x = q.y; |
| F[0].y = q.z; F[1].y = q.w; F[2].y =-q.x; |
| F[0].z =-q.y; F[1].z = q.x; F[2].z = q.w; |
| F[0].w =-q.x; F[1].w =-q.y; F[2].w =-q.z; |
| return F; |
| } |
| |
| void Fusion::predict(const vec3_t& w, float dT) { |
| const vec4_t q = x0; |
| const vec3_t b = x1; |
| const vec3_t we = w - b; |
| const vec4_t dq = getF(q)*((0.5f*dT)*we); |
| x0 = normalize_quat(q + dq); |
| |
| // P(k+1) = F*P(k)*Ft + G*Q*Gt |
| |
| // Phi = | Phi00 Phi10 | |
| // | 0 1 | |
| const mat33_t I33(1); |
| const mat33_t I33dT(dT); |
| const mat33_t wx(crossMatrix(we, 0)); |
| const mat33_t wx2(wx*wx); |
| const float lwedT = length(we)*dT; |
| const float ilwe = 1/length(we); |
| const float k0 = (1-cosf(lwedT))*(ilwe*ilwe); |
| const float k1 = sinf(lwedT); |
| |
| Phi[0][0] = I33 - wx*(k1*ilwe) + wx2*k0; |
| Phi[1][0] = wx*k0 - I33dT - wx2*(ilwe*ilwe*ilwe)*(lwedT-k1); |
| |
| P = Phi*P*transpose(Phi) + GQGt; |
| |
| checkState(); |
| } |
| |
| void Fusion::update(const vec3_t& z, const vec3_t& Bi, float sigma) { |
| vec4_t q(x0); |
| // measured vector in body space: h(p) = A(p)*Bi |
| const mat33_t A(quatToMatrix(q)); |
| const vec3_t Bb(A*Bi); |
| |
| // Sensitivity matrix H = dh(p)/dp |
| // H = [ L 0 ] |
| const mat33_t L(crossMatrix(Bb, 0)); |
| |
| // gain... |
| // K = P*Ht / [H*P*Ht + R] |
| vec<mat33_t, 2> K; |
| const mat33_t R(sigma*sigma); |
| const mat33_t S(scaleCovariance(L, P[0][0]) + R); |
| const mat33_t Si(invert(S)); |
| const mat33_t LtSi(transpose(L)*Si); |
| K[0] = P[0][0] * LtSi; |
| K[1] = transpose(P[1][0])*LtSi; |
| |
| // update... |
| // P -= K*H*P; |
| const mat33_t K0L(K[0] * L); |
| const mat33_t K1L(K[1] * L); |
| P[0][0] -= K0L*P[0][0]; |
| P[1][1] -= K1L*P[1][0]; |
| P[1][0] -= K0L*P[1][0]; |
| P[0][1] = transpose(P[1][0]); |
| |
| const vec3_t e(z - Bb); |
| const vec3_t dq(K[0]*e); |
| const vec3_t db(K[1]*e); |
| |
| q += getF(q)*(0.5f*dq); |
| x0 = normalize_quat(q); |
| x1 += db; |
| |
| checkState(); |
| } |
| |
| // ----------------------------------------------------------------------- |
| |
| }; // namespace android |
| |