services/sensorservice/Fusion.cpp - platform/frameworks/base - Gitiles

 /*
  * 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 {

 // -----------------------------------------------------------------------

 static const float gyroSTDEV = 3.16e-4; // rad/s^3/2
 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 biasSTDEV = 3.16e-5; // rad/s^1/2 (guessed)

 static const float FREE_FALL_THRESHOLD = 0.981f;

 // -----------------------------------------------------------------------

 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 = | -1 0 |
     //      |  0 1 |

     const float v = gyroSTDEV;
     const float u = biasSTDEV;
     const float q00 = v*v*dT + 0.33333f*(dT*dT*dT)*u*u;
     const float q10 =              0.5f*(dT*dT)   *u*u;
     const float q01 = q10;
     const float q11 = u*u*dT;
     GQGt[0][0] =  q00;
     GQGt[1][0] = -q10;
     GQGt[0][1] = -q01;
     GQGt[1][1] =  q11;


     // initial covariance: Var{ x(t0) }
     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(a) < FREE_FALL_THRESHOLD)
         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 obviously wrong magnetic-fields
     if (length(m) > 100)
         return BAD_VALUE;

     if (!checkInitComplete(MAG, m))
         return BAD_VALUE;

     const vec3_t up( getRotationMatrix() * Ba );
     const vec3_t east( cross_product(m, up) );
     vec3_t north( cross_product(up, east) );

     const float l = 1 / length(north);
     north *= l;

     update(north, Bm, magSTDEV*l);
     return NO_ERROR;
 }

 bool Fusion::checkState(const vec3_t& v) {
     if (isnanf(length(v))) {
         LOGW("9-axis fusion diverged. reseting state.");
         P = 0;
         x1 = 0;
         mInitState = 0;
         mCount[0] = 0;
         mCount[1] = 0;
         mCount[2] = 0;
         mData = 0;
         return false;
     }
     return true;
 }

 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;
 }

 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;
 }

 // -----------------------------------------------------------------------

 }; // namespace android
	/*
	* 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 {

	// -----------------------------------------------------------------------

	static const float gyroSTDEV = 3.16e-4; // rad/s^3/2
	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 biasSTDEV = 3.16e-5; // rad/s^1/2 (guessed)

	static const float FREE_FALL_THRESHOLD = 0.981f;

	// -----------------------------------------------------------------------

	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) {
	// APtranspose(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 mSumXXN - (mSumXtranspose(mSumX))(NN);
	}
	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 = \| -1 0 \|
	// \| 0 1 \|

	const float v = gyroSTDEV;
	const float u = biasSTDEV;
	const float q00 = vvdT + 0.33333f(dTdTdT)u*u;
	const float q10 = 0.5f(dTdT) uu;
	const float q01 = q10;
	const float q11 = uudT;
	GQGt[0][0] = q00;
	GQGt[1][0] = -q10;
	GQGt[0][1] = -q01;
	GQGt[1][1] = q11;


	// initial covariance: Var{ x(t0) }
	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(a) < FREE_FALL_THRESHOLD)
	return BAD_VALUE;

	if (!checkInitComplete(ACC, a))
	return BAD_VALUE;

	const float l = 1/length(a);
	update(al, Ba, accSTDEVl);
	return NO_ERROR;
	}

	status_t Fusion::handleMag(const vec3_t& m) {
	// the geomagnetic-field should be between 30uT and 60uT
	// reject obviously wrong magnetic-fields
	if (length(m) > 100)
	return BAD_VALUE;

	if (!checkInitComplete(MAG, m))
	return BAD_VALUE;

	const vec3_t up( getRotationMatrix() * Ba );
	const vec3_t east( cross_product(m, up) );
	vec3_t north( cross_product(up, east) );

	const float l = 1 / length(north);
	north *= l;

	update(north, Bm, magSTDEV*l);
	return NO_ERROR;
	}

	bool Fusion::checkState(const vec3_t& v) {
	if (isnanf(length(v))) {
	LOGW("9-axis fusion diverged. reseting state.");
	P = 0;
	x1 = 0;
	mInitState = 0;
	mCount[0] = 0;
	mCount[1] = 0;
	mCount[2] = 0;
	mData = 0;
	return false;
	}
	return true;
	}

	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)((0.5fdT)*we);
	x0 = normalize_quat(q + dq);

	// P(k+1) = FP(k)Ft + GQGt

	// 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))(ilweilwe);
	const float k1 = sinf(lwedT);

	Phi[0][0] = I33 - wx(k1ilwe) + wx2*k0;
	Phi[1][0] = wxk0 - I33dT - wx2(ilweilweilwe)*(lwedT-k1);

	P = PhiPtranspose(Phi) + GQGt;
	}

	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 = PHt / [HP*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 -= KHP;
	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)(0.5fdq);
	x0 = normalize_quat(q);
	x1 += db;
	}

	// -----------------------------------------------------------------------

	}; // namespace android