374 lines
9.3 KiB
C++
374 lines
9.3 KiB
C++
/*
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* Copyright (C) 2011 The Android Open Source Project
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*
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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*/
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#include <stdio.h>
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#include <utils/Log.h>
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#include "Fusion.h"
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namespace android {
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// -----------------------------------------------------------------------
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/*
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* gyroVAR gives the measured variance of the gyro's output per
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* Hz (or variance at 1 Hz). This is an "intrinsic" parameter of the gyro,
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* which is independent of the sampling frequency.
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*
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* The variance of gyro's output at a given sampling period can be
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* calculated as:
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* variance(T) = gyroVAR / T
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*
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* The variance of the INTEGRATED OUTPUT at a given sampling period can be
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* calculated as:
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* variance_integrate_output(T) = gyroVAR * T
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*
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*/
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static const float gyroVAR = 1e-7; // (rad/s)^2 / Hz
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static const float biasVAR = 1e-8; // (rad/s)^2 / s (guessed)
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/*
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* Standard deviations of accelerometer and magnetometer
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*/
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static const float accSTDEV = 0.05f; // m/s^2 (measured 0.08 / CDD 0.05)
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static const float magSTDEV = 0.5f; // uT (measured 0.7 / CDD 0.5)
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static const float FREE_FALL_THRESHOLD = 0.981f;
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// -----------------------------------------------------------------------
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template <typename TYPE, size_t C, size_t R>
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static mat<TYPE, R, R> scaleCovariance(
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const mat<TYPE, C, R>& A,
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const mat<TYPE, C, C>& P) {
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// A*P*transpose(A);
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mat<TYPE, R, R> APAt;
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for (size_t r=0 ; r<R ; r++) {
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for (size_t j=r ; j<R ; j++) {
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double apat(0);
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for (size_t c=0 ; c<C ; c++) {
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double v(A[c][r]*P[c][c]*0.5);
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for (size_t k=c+1 ; k<C ; k++)
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v += A[k][r] * P[c][k];
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apat += 2 * v * A[c][j];
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}
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APAt[j][r] = apat;
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APAt[r][j] = apat;
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}
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}
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return APAt;
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}
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template <typename TYPE, typename OTHER_TYPE>
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static mat<TYPE, 3, 3> crossMatrix(const vec<TYPE, 3>& p, OTHER_TYPE diag) {
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mat<TYPE, 3, 3> r;
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r[0][0] = diag;
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r[1][1] = diag;
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r[2][2] = diag;
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r[0][1] = p.z;
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r[1][0] =-p.z;
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r[0][2] =-p.y;
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r[2][0] = p.y;
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r[1][2] = p.x;
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r[2][1] =-p.x;
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return r;
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}
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template<typename TYPE, size_t SIZE>
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class Covariance {
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mat<TYPE, SIZE, SIZE> mSumXX;
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vec<TYPE, SIZE> mSumX;
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size_t mN;
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public:
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Covariance() : mSumXX(0.0f), mSumX(0.0f), mN(0) { }
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void update(const vec<TYPE, SIZE>& x) {
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mSumXX += x*transpose(x);
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mSumX += x;
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mN++;
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}
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mat<TYPE, SIZE, SIZE> operator()() const {
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const float N = 1.0f / mN;
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return mSumXX*N - (mSumX*transpose(mSumX))*(N*N);
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}
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void reset() {
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mN = 0;
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mSumXX = 0;
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mSumX = 0;
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}
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size_t getCount() const {
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return mN;
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}
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};
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// -----------------------------------------------------------------------
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Fusion::Fusion() {
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Phi[0][1] = 0;
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Phi[1][1] = 1;
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Ba.x = 0;
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Ba.y = 0;
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Ba.z = 1;
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Bm.x = 0;
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Bm.y = 1;
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Bm.z = 0;
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init();
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}
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void Fusion::init() {
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mInitState = 0;
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mGyroRate = 0;
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mCount[0] = 0;
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mCount[1] = 0;
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mCount[2] = 0;
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mData = 0;
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}
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void Fusion::initFusion(const vec4_t& q, float dT)
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{
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// initial estimate: E{ x(t0) }
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x0 = q;
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x1 = 0;
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// process noise covariance matrix: G.Q.Gt, with
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//
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// G = | -1 0 | Q = | q00 q10 |
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// | 0 1 | | q01 q11 |
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//
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// q00 = sv^2.dt + 1/3.su^2.dt^3
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// q10 = q01 = 1/2.su^2.dt^2
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// q11 = su^2.dt
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//
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// variance of integrated output at 1/dT Hz
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// (random drift)
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const float q00 = gyroVAR * dT;
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// variance of drift rate ramp
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const float q11 = biasVAR * dT;
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const float u = q11 / dT;
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const float q10 = 0.5f*u*dT*dT;
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const float q01 = q10;
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GQGt[0][0] = q00; // rad^2
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GQGt[1][0] = -q10;
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GQGt[0][1] = -q01;
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GQGt[1][1] = q11; // (rad/s)^2
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// initial covariance: Var{ x(t0) }
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// TODO: initialize P correctly
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P = 0;
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}
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bool Fusion::hasEstimate() const {
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return (mInitState == (MAG|ACC|GYRO));
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}
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bool Fusion::checkInitComplete(int what, const vec3_t& d, float dT) {
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if (hasEstimate())
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return true;
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if (what == ACC) {
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mData[0] += d * (1/length(d));
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mCount[0]++;
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mInitState |= ACC;
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} else if (what == MAG) {
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mData[1] += d * (1/length(d));
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mCount[1]++;
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mInitState |= MAG;
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} else if (what == GYRO) {
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mGyroRate = dT;
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mData[2] += d*dT;
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mCount[2]++;
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if (mCount[2] == 64) {
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// 64 samples is good enough to estimate the gyro drift and
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// doesn't take too much time.
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mInitState |= GYRO;
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}
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}
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if (mInitState == (MAG|ACC|GYRO)) {
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// Average all the values we collected so far
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mData[0] *= 1.0f/mCount[0];
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mData[1] *= 1.0f/mCount[1];
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mData[2] *= 1.0f/mCount[2];
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// calculate the MRPs from the data collection, this gives us
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// a rough estimate of our initial state
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mat33_t R;
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vec3_t up(mData[0]);
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vec3_t east(cross_product(mData[1], up));
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east *= 1/length(east);
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vec3_t north(cross_product(up, east));
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R << east << north << up;
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const vec4_t q = matrixToQuat(R);
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initFusion(q, mGyroRate);
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}
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return false;
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}
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void Fusion::handleGyro(const vec3_t& w, float dT) {
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if (!checkInitComplete(GYRO, w, dT))
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return;
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predict(w, dT);
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}
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status_t Fusion::handleAcc(const vec3_t& a) {
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// ignore acceleration data if we're close to free-fall
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if (length(a) < FREE_FALL_THRESHOLD)
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return BAD_VALUE;
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if (!checkInitComplete(ACC, a))
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return BAD_VALUE;
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const float l = 1/length(a);
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update(a*l, Ba, accSTDEV*l);
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return NO_ERROR;
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}
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status_t Fusion::handleMag(const vec3_t& m) {
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// the geomagnetic-field should be between 30uT and 60uT
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// reject obviously wrong magnetic-fields
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if (length(m) > 100)
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return BAD_VALUE;
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if (!checkInitComplete(MAG, m))
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return BAD_VALUE;
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const vec3_t up( getRotationMatrix() * Ba );
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const vec3_t east( cross_product(m, up) );
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vec3_t north( cross_product(up, east) );
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const float l = 1 / length(north);
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north *= l;
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update(north, Bm, magSTDEV*l);
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return NO_ERROR;
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}
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bool Fusion::checkState(const vec3_t& v) {
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if (isnanf(length(v))) {
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LOGW("9-axis fusion diverged. reseting state.");
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P = 0;
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x1 = 0;
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mInitState = 0;
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mCount[0] = 0;
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mCount[1] = 0;
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mCount[2] = 0;
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mData = 0;
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return false;
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}
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return true;
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}
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vec4_t Fusion::getAttitude() const {
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return x0;
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}
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vec3_t Fusion::getBias() const {
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return x1;
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}
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mat33_t Fusion::getRotationMatrix() const {
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return quatToMatrix(x0);
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}
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mat34_t Fusion::getF(const vec4_t& q) {
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mat34_t F;
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F[0].x = q.w; F[1].x =-q.z; F[2].x = q.y;
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F[0].y = q.z; F[1].y = q.w; F[2].y =-q.x;
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F[0].z =-q.y; F[1].z = q.x; F[2].z = q.w;
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F[0].w =-q.x; F[1].w =-q.y; F[2].w =-q.z;
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return F;
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}
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void Fusion::predict(const vec3_t& w, float dT) {
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const vec4_t q = x0;
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const vec3_t b = x1;
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const vec3_t we = w - b;
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const vec4_t dq = getF(q)*((0.5f*dT)*we);
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x0 = normalize_quat(q + dq);
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// P(k+1) = F*P(k)*Ft + G*Q*Gt
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// Phi = | Phi00 Phi10 |
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// | 0 1 |
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const mat33_t I33(1);
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const mat33_t I33dT(dT);
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const mat33_t wx(crossMatrix(we, 0));
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const mat33_t wx2(wx*wx);
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const float lwedT = length(we)*dT;
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const float ilwe = 1/length(we);
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const float k0 = (1-cosf(lwedT))*(ilwe*ilwe);
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const float k1 = sinf(lwedT);
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Phi[0][0] = I33 - wx*(k1*ilwe) + wx2*k0;
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Phi[1][0] = wx*k0 - I33dT - wx2*(ilwe*ilwe*ilwe)*(lwedT-k1);
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P = Phi*P*transpose(Phi) + GQGt;
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}
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void Fusion::update(const vec3_t& z, const vec3_t& Bi, float sigma) {
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vec4_t q(x0);
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// measured vector in body space: h(p) = A(p)*Bi
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const mat33_t A(quatToMatrix(q));
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const vec3_t Bb(A*Bi);
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// Sensitivity matrix H = dh(p)/dp
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// H = [ L 0 ]
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const mat33_t L(crossMatrix(Bb, 0));
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// gain...
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// K = P*Ht / [H*P*Ht + R]
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vec<mat33_t, 2> K;
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const mat33_t R(sigma*sigma);
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const mat33_t S(scaleCovariance(L, P[0][0]) + R);
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const mat33_t Si(invert(S));
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const mat33_t LtSi(transpose(L)*Si);
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K[0] = P[0][0] * LtSi;
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K[1] = transpose(P[1][0])*LtSi;
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// update...
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// P -= K*H*P;
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const mat33_t K0L(K[0] * L);
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const mat33_t K1L(K[1] * L);
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P[0][0] -= K0L*P[0][0];
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P[1][1] -= K1L*P[1][0];
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P[1][0] -= K0L*P[1][0];
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P[0][1] = transpose(P[1][0]);
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const vec3_t e(z - Bb);
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const vec3_t dq(K[0]*e);
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const vec3_t db(K[1]*e);
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q += getF(q)*(0.5f*dq);
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x0 = normalize_quat(q);
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x1 += db;
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}
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// -----------------------------------------------------------------------
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}; // namespace android
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