/**************************************************************************** * * Copyright (c) 2015 Estimation and Control Library (ECL). All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions * are met: * * 1. Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * 2. Redistributions in binary form must reproduce the above copyright * notice, this list of conditions and the following disclaimer in * the documentation and/or other materials provided with the * distribution. * 3. Neither the name ECL nor the names of its contributors may be * used to endorse or promote products derived from this software * without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS * "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT * LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS * FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE * COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, * INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, * BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS * OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED * AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT * LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN * ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE * POSSIBILITY OF SUCH DAMAGE. * ****************************************************************************/ /** * @file vel_pos_fusion.cpp * Function for fusing gps and baro measurements/ * * @author Paul Riseborough * @author Siddharth Bharat Purohit * */ #include "ekf.h" #include "mathlib.h" void Ekf::fuseOptFlow() { float gndclearance = fmaxf(_params.rng_gnd_clearance, 0.1f); float optflow_test_ratio[2] = {0}; // get latest estimated orientation float q0 = _state.quat_nominal(0); float q1 = _state.quat_nominal(1); float q2 = _state.quat_nominal(2); float q3 = _state.quat_nominal(3); // get latest velocity in earth frame float vn = _state.vel(0); float ve = _state.vel(1); float vd = _state.vel(2); // calculate the optical flow observation variance float R_LOS = calcOptFlowMeasVar(); float H_LOS[2][24] = {}; // Optical flow observation Jacobians float Kfusion[24][2] = {}; // Optical flow Kalman gains // constrain height above ground to be above minimum height when sitting on ground float heightAboveGndEst = math::max((_terrain_vpos - _state.pos(2)), gndclearance); // get rotation nmatrix from earth to body Dcmf earth_to_body(_state.quat_nominal); earth_to_body = earth_to_body.transpose(); // calculate the sensor position relative to the IMU Vector3f pos_offset_body = _params.flow_pos_body - _params.imu_pos_body; // calculate the velocity of the sensor reelative to the imu in body frame Vector3f vel_rel_imu_body = cross_product(_flow_sample_delayed.gyroXYZ, pos_offset_body); // calculate the velocity of the sensor in the earth frame Vector3f vel_rel_earth = _state.vel + _R_to_earth * vel_rel_imu_body; // rotate into body frame Vector3f vel_body = earth_to_body * vel_rel_earth; // calculate range from focal point to centre of image float range = heightAboveGndEst / earth_to_body(2, 2); // absolute distance to the frame region in view // calculate optical LOS rates using optical flow rates that have had the body angular rate contribution removed // correct for gyro bias errors in the data used to do the motion compensation // Note the sign convention used: A positive LOS rate is a RH rotaton of the scene about that axis. Vector2f opt_flow_rate; opt_flow_rate(0) = _flow_sample_delayed.flowRadXYcomp(0) / _flow_sample_delayed.dt + _flow_gyro_bias(0); opt_flow_rate(1) = _flow_sample_delayed.flowRadXYcomp(1) / _flow_sample_delayed.dt + _flow_gyro_bias(1); if (opt_flow_rate.norm() < _params.flow_rate_max) { _flow_innov[0] = vel_body(1) / range - opt_flow_rate(0); // flow around the X axis _flow_innov[1] = -vel_body(0) / range - opt_flow_rate(1); // flow around the Y axis } else { return; } // Fuse X and Y axis measurements sequentially assuming observation errors are uncorrelated // Calculate Obser ation Jacobians and Kalman gans for each measurement axis for (uint8_t obs_index = 0; obs_index <= 1; obs_index++) { if (obs_index == 0) { // calculate X axis observation Jacobian float t2 = 1.0f / range; H_LOS[0][0] = t2*(q1*vd*2.0f+q0*ve*2.0f-q3*vn*2.0f); H_LOS[0][1] = t2*(q0*vd*2.0f-q1*ve*2.0f+q2*vn*2.0f); H_LOS[0][2] = t2*(q3*vd*2.0f+q2*ve*2.0f+q1*vn*2.0f); H_LOS[0][3] = -t2*(q2*vd*-2.0f+q3*ve*2.0f+q0*vn*2.0f); H_LOS[0][4] = -t2*(q0*q3*2.0f-q1*q2*2.0f); H_LOS[0][5] = t2*(q0*q0-q1*q1+q2*q2-q3*q3); H_LOS[0][6] = t2*(q0*q1*2.0f+q2*q3*2.0f); // calculate intermediate variables for the X observaton innovatoin variance and Kalman gains float t3 = q1*vd*2.0f; float t4 = q0*ve*2.0f; float t11 = q3*vn*2.0f; float t5 = t3+t4-t11; float t6 = q0*q3*2.0f; float t29 = q1*q2*2.0f; float t7 = t6-t29; float t8 = q0*q1*2.0f; float t9 = q2*q3*2.0f; float t10 = t8+t9; float t12 = P[0][0]*t2*t5; float t13 = q0*vd*2.0f; float t14 = q2*vn*2.0f; float t28 = q1*ve*2.0f; float t15 = t13+t14-t28; float t16 = q3*vd*2.0f; float t17 = q2*ve*2.0f; float t18 = q1*vn*2.0f; float t19 = t16+t17+t18; float t20 = q3*ve*2.0f; float t21 = q0*vn*2.0f; float t30 = q2*vd*2.0f; float t22 = t20+t21-t30; float t23 = q0*q0; float t24 = q1*q1; float t25 = q2*q2; float t26 = q3*q3; float t27 = t23-t24+t25-t26; float t31 = P[1][1]*t2*t15; float t32 = P[6][0]*t2*t10; float t33 = P[1][0]*t2*t15; float t34 = P[2][0]*t2*t19; float t35 = P[5][0]*t2*t27; float t79 = P[4][0]*t2*t7; float t80 = P[3][0]*t2*t22; float t36 = t12+t32+t33+t34+t35-t79-t80; float t37 = t2*t5*t36; float t38 = P[6][1]*t2*t10; float t39 = P[0][1]*t2*t5; float t40 = P[2][1]*t2*t19; float t41 = P[5][1]*t2*t27; float t81 = P[4][1]*t2*t7; float t82 = P[3][1]*t2*t22; float t42 = t31+t38+t39+t40+t41-t81-t82; float t43 = t2*t15*t42; float t44 = P[6][2]*t2*t10; float t45 = P[0][2]*t2*t5; float t46 = P[1][2]*t2*t15; float t47 = P[2][2]*t2*t19; float t48 = P[5][2]*t2*t27; float t83 = P[4][2]*t2*t7; float t84 = P[3][2]*t2*t22; float t49 = t44+t45+t46+t47+t48-t83-t84; float t50 = t2*t19*t49; float t51 = P[6][3]*t2*t10; float t52 = P[0][3]*t2*t5; float t53 = P[1][3]*t2*t15; float t54 = P[2][3]*t2*t19; float t55 = P[5][3]*t2*t27; float t85 = P[4][3]*t2*t7; float t86 = P[3][3]*t2*t22; float t56 = t51+t52+t53+t54+t55-t85-t86; float t57 = P[6][5]*t2*t10; float t58 = P[0][5]*t2*t5; float t59 = P[1][5]*t2*t15; float t60 = P[2][5]*t2*t19; float t61 = P[5][5]*t2*t27; float t88 = P[4][5]*t2*t7; float t89 = P[3][5]*t2*t22; float t62 = t57+t58+t59+t60+t61-t88-t89; float t63 = t2*t27*t62; float t64 = P[6][4]*t2*t10; float t65 = P[0][4]*t2*t5; float t66 = P[1][4]*t2*t15; float t67 = P[2][4]*t2*t19; float t68 = P[5][4]*t2*t27; float t90 = P[4][4]*t2*t7; float t91 = P[3][4]*t2*t22; float t69 = t64+t65+t66+t67+t68-t90-t91; float t70 = P[6][6]*t2*t10; float t71 = P[0][6]*t2*t5; float t72 = P[1][6]*t2*t15; float t73 = P[2][6]*t2*t19; float t74 = P[5][6]*t2*t27; float t93 = P[4][6]*t2*t7; float t94 = P[3][6]*t2*t22; float t75 = t70+t71+t72+t73+t74-t93-t94; float t76 = t2*t10*t75; float t87 = t2*t22*t56; float t92 = t2*t7*t69; float t77 = R_LOS+t37+t43+t50+t63+t76-t87-t92; float t78; // calculate innovation variance for X axis observation and protect against a badly conditioned calculation if (t77 >= R_LOS) { t78 = 1.0f / t77; _flow_innov_var[0] = t77; } else { // we need to reinitialise the covariance matrix and abort this fusion step initialiseCovariance(); return; } // calculate Kalman gains for X-axis observation Kfusion[0][0] = t78*(t12-P[0][4]*t2*t7+P[0][1]*t2*t15+P[0][6]*t2*t10+P[0][2]*t2*t19-P[0][3]*t2*t22+P[0][5]*t2*t27); Kfusion[1][0] = t78*(t31+P[1][0]*t2*t5-P[1][4]*t2*t7+P[1][6]*t2*t10+P[1][2]*t2*t19-P[1][3]*t2*t22+P[1][5]*t2*t27); Kfusion[2][0] = t78*(t47+P[2][0]*t2*t5-P[2][4]*t2*t7+P[2][1]*t2*t15+P[2][6]*t2*t10-P[2][3]*t2*t22+P[2][5]*t2*t27); Kfusion[3][0] = t78*(-t86+P[3][0]*t2*t5-P[3][4]*t2*t7+P[3][1]*t2*t15+P[3][6]*t2*t10+P[3][2]*t2*t19+P[3][5]*t2*t27); Kfusion[4][0] = t78*(-t90+P[4][0]*t2*t5+P[4][1]*t2*t15+P[4][6]*t2*t10+P[4][2]*t2*t19-P[4][3]*t2*t22+P[4][5]*t2*t27); Kfusion[5][0] = t78*(t61+P[5][0]*t2*t5-P[5][4]*t2*t7+P[5][1]*t2*t15+P[5][6]*t2*t10+P[5][2]*t2*t19-P[5][3]*t2*t22); Kfusion[6][0] = t78*(t70+P[6][0]*t2*t5-P[6][4]*t2*t7+P[6][1]*t2*t15+P[6][2]*t2*t19-P[6][3]*t2*t22+P[6][5]*t2*t27); Kfusion[7][0] = t78*(P[7][0]*t2*t5-P[7][4]*t2*t7+P[7][1]*t2*t15+P[7][6]*t2*t10+P[7][2]*t2*t19-P[7][3]*t2*t22+P[7][5]*t2*t27); Kfusion[8][0] = t78*(P[8][0]*t2*t5-P[8][4]*t2*t7+P[8][1]*t2*t15+P[8][6]*t2*t10+P[8][2]*t2*t19-P[8][3]*t2*t22+P[8][5]*t2*t27); Kfusion[9][0] = t78*(P[9][0]*t2*t5-P[9][4]*t2*t7+P[9][1]*t2*t15+P[9][6]*t2*t10+P[9][2]*t2*t19-P[9][3]*t2*t22+P[9][5]*t2*t27); Kfusion[10][0] = t78*(P[10][0]*t2*t5-P[10][4]*t2*t7+P[10][1]*t2*t15+P[10][6]*t2*t10+P[10][2]*t2*t19-P[10][3]*t2*t22+P[10][5]*t2*t27); Kfusion[11][0] = t78*(P[11][0]*t2*t5-P[11][4]*t2*t7+P[11][1]*t2*t15+P[11][6]*t2*t10+P[11][2]*t2*t19-P[11][3]*t2*t22+P[11][5]*t2*t27); Kfusion[12][0] = t78*(P[12][0]*t2*t5-P[12][4]*t2*t7+P[12][1]*t2*t15+P[12][6]*t2*t10+P[12][2]*t2*t19-P[12][3]*t2*t22+P[12][5]*t2*t27); Kfusion[13][0] = t78*(P[13][0]*t2*t5-P[13][4]*t2*t7+P[13][1]*t2*t15+P[13][6]*t2*t10+P[13][2]*t2*t19-P[13][3]*t2*t22+P[13][5]*t2*t27); Kfusion[14][0] = t78*(P[14][0]*t2*t5-P[14][4]*t2*t7+P[14][1]*t2*t15+P[14][6]*t2*t10+P[14][2]*t2*t19-P[14][3]*t2*t22+P[14][5]*t2*t27); Kfusion[15][0] = t78*(P[15][0]*t2*t5-P[15][4]*t2*t7+P[15][1]*t2*t15+P[15][6]*t2*t10+P[15][2]*t2*t19-P[15][3]*t2*t22+P[15][5]*t2*t27); Kfusion[16][0] = t78*(P[16][0]*t2*t5-P[16][4]*t2*t7+P[16][1]*t2*t15+P[16][6]*t2*t10+P[16][2]*t2*t19-P[16][3]*t2*t22+P[16][5]*t2*t27); Kfusion[17][0] = t78*(P[17][0]*t2*t5-P[17][4]*t2*t7+P[17][1]*t2*t15+P[17][6]*t2*t10+P[17][2]*t2*t19-P[17][3]*t2*t22+P[17][5]*t2*t27); Kfusion[18][0] = t78*(P[18][0]*t2*t5-P[18][4]*t2*t7+P[18][1]*t2*t15+P[18][6]*t2*t10+P[18][2]*t2*t19-P[18][3]*t2*t22+P[18][5]*t2*t27); Kfusion[19][0] = t78*(P[19][0]*t2*t5-P[19][4]*t2*t7+P[19][1]*t2*t15+P[19][6]*t2*t10+P[19][2]*t2*t19-P[19][3]*t2*t22+P[19][5]*t2*t27); Kfusion[20][0] = t78*(P[20][0]*t2*t5-P[20][4]*t2*t7+P[20][1]*t2*t15+P[20][6]*t2*t10+P[20][2]*t2*t19-P[20][3]*t2*t22+P[20][5]*t2*t27); Kfusion[21][0] = t78*(P[21][0]*t2*t5-P[21][4]*t2*t7+P[21][1]*t2*t15+P[21][6]*t2*t10+P[21][2]*t2*t19-P[21][3]*t2*t22+P[21][5]*t2*t27); Kfusion[22][0] = t78*(P[22][0]*t2*t5-P[22][4]*t2*t7+P[22][1]*t2*t15+P[22][6]*t2*t10+P[22][2]*t2*t19-P[22][3]*t2*t22+P[22][5]*t2*t27); Kfusion[23][0] = t78*(P[23][0]*t2*t5-P[23][4]*t2*t7+P[23][1]*t2*t15+P[23][6]*t2*t10+P[23][2]*t2*t19-P[23][3]*t2*t22+P[23][5]*t2*t27); // run innovation consistency checks optflow_test_ratio[0] = sq(_flow_innov[0]) / (sq(math::max(_params.flow_innov_gate, 1.0f)) * _flow_innov_var[0]); } else if (obs_index == 1) { // calculate Y axis observation Jacobian float t2 = 1.0f / range; H_LOS[1][0] = -t2*(q2*vd*-2.0f+q3*ve*2.0f+q0*vn*2.0f); H_LOS[1][1] = -t2*(q3*vd*2.0f+q2*ve*2.0f+q1*vn*2.0f); H_LOS[1][2] = t2*(q0*vd*2.0f-q1*ve*2.0f+q2*vn*2.0f); H_LOS[1][3] = -t2*(q1*vd*2.0f+q0*ve*2.0f-q3*vn*2.0f); H_LOS[1][4] = -t2*(q0*q0+q1*q1-q2*q2-q3*q3); H_LOS[1][5] = -t2*(q0*q3*2.0f+q1*q2*2.0f); H_LOS[1][6] = t2*(q0*q2*2.0f-q1*q3*2.0f); // calculate intermediate variables for the Y observaton innovatoin variance and Kalman gains float t3 = q3*ve*2.0f; float t4 = q0*vn*2.0f; float t11 = q2*vd*2.0f; float t5 = t3+t4-t11; float t6 = q0*q3*2.0f; float t7 = q1*q2*2.0f; float t8 = t6+t7; float t9 = q0*q2*2.0f; float t28 = q1*q3*2.0f; float t10 = t9-t28; float t12 = P[0][0]*t2*t5; float t13 = q3*vd*2.0f; float t14 = q2*ve*2.0f; float t15 = q1*vn*2.0f; float t16 = t13+t14+t15; float t17 = q0*vd*2.0f; float t18 = q2*vn*2.0f; float t29 = q1*ve*2.0f; float t19 = t17+t18-t29; float t20 = q1*vd*2.0f; float t21 = q0*ve*2.0f; float t30 = q3*vn*2.0f; float t22 = t20+t21-t30; float t23 = q0*q0; float t24 = q1*q1; float t25 = q2*q2; float t26 = q3*q3; float t27 = t23+t24-t25-t26; float t31 = P[1][1]*t2*t16; float t32 = P[5][0]*t2*t8; float t33 = P[1][0]*t2*t16; float t34 = P[3][0]*t2*t22; float t35 = P[4][0]*t2*t27; float t80 = P[6][0]*t2*t10; float t81 = P[2][0]*t2*t19; float t36 = t12+t32+t33+t34+t35-t80-t81; float t37 = t2*t5*t36; float t38 = P[5][1]*t2*t8; float t39 = P[0][1]*t2*t5; float t40 = P[3][1]*t2*t22; float t41 = P[4][1]*t2*t27; float t82 = P[6][1]*t2*t10; float t83 = P[2][1]*t2*t19; float t42 = t31+t38+t39+t40+t41-t82-t83; float t43 = t2*t16*t42; float t44 = P[5][2]*t2*t8; float t45 = P[0][2]*t2*t5; float t46 = P[1][2]*t2*t16; float t47 = P[3][2]*t2*t22; float t48 = P[4][2]*t2*t27; float t79 = P[2][2]*t2*t19; float t84 = P[6][2]*t2*t10; float t49 = t44+t45+t46+t47+t48-t79-t84; float t50 = P[5][3]*t2*t8; float t51 = P[0][3]*t2*t5; float t52 = P[1][3]*t2*t16; float t53 = P[3][3]*t2*t22; float t54 = P[4][3]*t2*t27; float t86 = P[6][3]*t2*t10; float t87 = P[2][3]*t2*t19; float t55 = t50+t51+t52+t53+t54-t86-t87; float t56 = t2*t22*t55; float t57 = P[5][4]*t2*t8; float t58 = P[0][4]*t2*t5; float t59 = P[1][4]*t2*t16; float t60 = P[3][4]*t2*t22; float t61 = P[4][4]*t2*t27; float t88 = P[6][4]*t2*t10; float t89 = P[2][4]*t2*t19; float t62 = t57+t58+t59+t60+t61-t88-t89; float t63 = t2*t27*t62; float t64 = P[5][5]*t2*t8; float t65 = P[0][5]*t2*t5; float t66 = P[1][5]*t2*t16; float t67 = P[3][5]*t2*t22; float t68 = P[4][5]*t2*t27; float t90 = P[6][5]*t2*t10; float t91 = P[2][5]*t2*t19; float t69 = t64+t65+t66+t67+t68-t90-t91; float t70 = t2*t8*t69; float t71 = P[5][6]*t2*t8; float t72 = P[0][6]*t2*t5; float t73 = P[1][6]*t2*t16; float t74 = P[3][6]*t2*t22; float t75 = P[4][6]*t2*t27; float t92 = P[6][6]*t2*t10; float t93 = P[2][6]*t2*t19; float t76 = t71+t72+t73+t74+t75-t92-t93; float t85 = t2*t19*t49; float t94 = t2*t10*t76; float t77 = R_LOS+t37+t43+t56+t63+t70-t85-t94; float t78; // calculate innovation variance for Y axis observation and protect against a badly conditioned calculation if (t77 >= R_LOS) { t78 = 1.0f / t77; _flow_innov_var[1] = t77; } else { // we need to reinitialise the covariance matrix and abort this fusion step initialiseCovariance(); return; } // calculate Kalman gains for Y-axis observation Kfusion[0][1] = -t78*(t12+P[0][5]*t2*t8-P[0][6]*t2*t10+P[0][1]*t2*t16-P[0][2]*t2*t19+P[0][3]*t2*t22+P[0][4]*t2*t27); Kfusion[1][1] = -t78*(t31+P[1][0]*t2*t5+P[1][5]*t2*t8-P[1][6]*t2*t10-P[1][2]*t2*t19+P[1][3]*t2*t22+P[1][4]*t2*t27); Kfusion[2][1] = -t78*(-t79+P[2][0]*t2*t5+P[2][5]*t2*t8-P[2][6]*t2*t10+P[2][1]*t2*t16+P[2][3]*t2*t22+P[2][4]*t2*t27); Kfusion[3][1] = -t78*(t53+P[3][0]*t2*t5+P[3][5]*t2*t8-P[3][6]*t2*t10+P[3][1]*t2*t16-P[3][2]*t2*t19+P[3][4]*t2*t27); Kfusion[4][1] = -t78*(t61+P[4][0]*t2*t5+P[4][5]*t2*t8-P[4][6]*t2*t10+P[4][1]*t2*t16-P[4][2]*t2*t19+P[4][3]*t2*t22); Kfusion[5][1] = -t78*(t64+P[5][0]*t2*t5-P[5][6]*t2*t10+P[5][1]*t2*t16-P[5][2]*t2*t19+P[5][3]*t2*t22+P[5][4]*t2*t27); Kfusion[6][1] = -t78*(-t92+P[6][0]*t2*t5+P[6][5]*t2*t8+P[6][1]*t2*t16-P[6][2]*t2*t19+P[6][3]*t2*t22+P[6][4]*t2*t27); Kfusion[7][1] = -t78*(P[7][0]*t2*t5+P[7][5]*t2*t8-P[7][6]*t2*t10+P[7][1]*t2*t16-P[7][2]*t2*t19+P[7][3]*t2*t22+P[7][4]*t2*t27); Kfusion[8][1] = -t78*(P[8][0]*t2*t5+P[8][5]*t2*t8-P[8][6]*t2*t10+P[8][1]*t2*t16-P[8][2]*t2*t19+P[8][3]*t2*t22+P[8][4]*t2*t27); Kfusion[9][1] = -t78*(P[9][0]*t2*t5+P[9][5]*t2*t8-P[9][6]*t2*t10+P[9][1]*t2*t16-P[9][2]*t2*t19+P[9][3]*t2*t22+P[9][4]*t2*t27); Kfusion[10][1] = -t78*(P[10][0]*t2*t5+P[10][5]*t2*t8-P[10][6]*t2*t10+P[10][1]*t2*t16-P[10][2]*t2*t19+P[10][3]*t2*t22+P[10][4]*t2*t27); Kfusion[11][1] = -t78*(P[11][0]*t2*t5+P[11][5]*t2*t8-P[11][6]*t2*t10+P[11][1]*t2*t16-P[11][2]*t2*t19+P[11][3]*t2*t22+P[11][4]*t2*t27); Kfusion[12][1] = -t78*(P[12][0]*t2*t5+P[12][5]*t2*t8-P[12][6]*t2*t10+P[12][1]*t2*t16-P[12][2]*t2*t19+P[12][3]*t2*t22+P[12][4]*t2*t27); Kfusion[13][1] = -t78*(P[13][0]*t2*t5+P[13][5]*t2*t8-P[13][6]*t2*t10+P[13][1]*t2*t16-P[13][2]*t2*t19+P[13][3]*t2*t22+P[13][4]*t2*t27); Kfusion[14][1] = -t78*(P[14][0]*t2*t5+P[14][5]*t2*t8-P[14][6]*t2*t10+P[14][1]*t2*t16-P[14][2]*t2*t19+P[14][3]*t2*t22+P[14][4]*t2*t27); Kfusion[15][1] = -t78*(P[15][0]*t2*t5+P[15][5]*t2*t8-P[15][6]*t2*t10+P[15][1]*t2*t16-P[15][2]*t2*t19+P[15][3]*t2*t22+P[15][4]*t2*t27); Kfusion[16][1] = -t78*(P[16][0]*t2*t5+P[16][5]*t2*t8-P[16][6]*t2*t10+P[16][1]*t2*t16-P[16][2]*t2*t19+P[16][3]*t2*t22+P[16][4]*t2*t27); Kfusion[17][1] = -t78*(P[17][0]*t2*t5+P[17][5]*t2*t8-P[17][6]*t2*t10+P[17][1]*t2*t16-P[17][2]*t2*t19+P[17][3]*t2*t22+P[17][4]*t2*t27); Kfusion[18][1] = -t78*(P[18][0]*t2*t5+P[18][5]*t2*t8-P[18][6]*t2*t10+P[18][1]*t2*t16-P[18][2]*t2*t19+P[18][3]*t2*t22+P[18][4]*t2*t27); Kfusion[19][1] = -t78*(P[19][0]*t2*t5+P[19][5]*t2*t8-P[19][6]*t2*t10+P[19][1]*t2*t16-P[19][2]*t2*t19+P[19][3]*t2*t22+P[19][4]*t2*t27); Kfusion[20][1] = -t78*(P[20][0]*t2*t5+P[20][5]*t2*t8-P[20][6]*t2*t10+P[20][1]*t2*t16-P[20][2]*t2*t19+P[20][3]*t2*t22+P[20][4]*t2*t27); Kfusion[21][1] = -t78*(P[21][0]*t2*t5+P[21][5]*t2*t8-P[21][6]*t2*t10+P[21][1]*t2*t16-P[21][2]*t2*t19+P[21][3]*t2*t22+P[21][4]*t2*t27); Kfusion[22][1] = -t78*(P[22][0]*t2*t5+P[22][5]*t2*t8-P[22][6]*t2*t10+P[22][1]*t2*t16-P[22][2]*t2*t19+P[22][3]*t2*t22+P[22][4]*t2*t27); Kfusion[23][1] = -t78*(P[23][0]*t2*t5+P[23][5]*t2*t8-P[23][6]*t2*t10+P[23][1]*t2*t16-P[23][2]*t2*t19+P[23][3]*t2*t22+P[23][4]*t2*t27); // run innovation consistency check optflow_test_ratio[1] = sq(_flow_innov[1]) / (sq(math::max(_params.flow_innov_gate, 1.0f)) * _flow_innov_var[1]); } } // record the innovation test pass/fail bool flow_fail = false; for (uint8_t obs_index = 0; obs_index <= 1; obs_index++) { if (optflow_test_ratio[obs_index] > 1.0f) { flow_fail = true; _innov_check_fail_status.value |= (1 << (obs_index + 10)); } else { _innov_check_fail_status.value &= ~(1 << (obs_index + 10)); } } // if either axis fails we abort the fusion if (flow_fail) { return; } for (uint8_t obs_index = 0; obs_index <= 1; obs_index++) { // copy the Kalman gain vector for the axis we are fusing float gain[24]; for (unsigned row = 0; row <= 23; row++) { gain[row] = Kfusion[row][obs_index]; } // apply covariance correction via P_new = (I -K*H)*P // first calculate expression for KHP // then calculate P - KHP float KHP[_k_num_states][_k_num_states]; float KH[7]; for (unsigned row = 0; row < _k_num_states; row++) { KH[0] = gain[row] * H_LOS[obs_index][0]; KH[1] = gain[row] * H_LOS[obs_index][1]; KH[2] = gain[row] * H_LOS[obs_index][2]; KH[3] = gain[row] * H_LOS[obs_index][3]; KH[4] = gain[row] * H_LOS[obs_index][4]; KH[5] = gain[row] * H_LOS[obs_index][5]; KH[6] = gain[row] * H_LOS[obs_index][6]; for (unsigned column = 0; column < _k_num_states; column++) { float tmp = KH[0] * P[0][column]; tmp += KH[1] * P[1][column]; tmp += KH[2] * P[2][column]; tmp += KH[3] * P[3][column]; tmp += KH[4] * P[4][column]; tmp += KH[5] * P[5][column]; tmp += KH[6] * P[6][column]; KHP[row][column] = tmp; } } // if the covariance correction will result in a negative variance, then // the covariance marix is unhealthy and must be corrected bool healthy = true; _fault_status.flags.bad_optflow_X = false; _fault_status.flags.bad_optflow_Y = false; for (int i = 0; i < _k_num_states; i++) { if (P[i][i] < KHP[i][i]) { // zero rows and columns zeroRows(P,i,i); zeroCols(P,i,i); //flag as unhealthy healthy = false; // update individual measurement health status if (obs_index == 0) { _fault_status.flags.bad_optflow_X = true; } else if (obs_index == 1) { _fault_status.flags.bad_optflow_Y = true; } } } // only apply covariance and state corrrections if healthy if (healthy) { // apply the covariance corrections for (unsigned row = 0; row < _k_num_states; row++) { for (unsigned column = 0; column < _k_num_states; column++) { P[row][column] = P[row][column] - KHP[row][column]; } } // correct the covariance marix for gross errors fixCovarianceErrors(); // apply the state corrections fuse(gain, _flow_innov[obs_index]); _time_last_of_fuse = _time_last_imu; _gps_check_fail_status.value = 0; } } } void Ekf::get_flow_innov(float flow_innov[2]) { memcpy(flow_innov, _flow_innov, sizeof(_flow_innov)); } void Ekf::get_flow_innov_var(float flow_innov_var[2]) { memcpy(flow_innov_var, _flow_innov_var, sizeof(_flow_innov_var)); } void Ekf::get_drag_innov(float drag_innov[2]) { memcpy(drag_innov, _drag_innov, sizeof(_drag_innov)); } void Ekf::get_drag_innov_var(float drag_innov_var[2]) { memcpy(drag_innov_var, _drag_innov_var, sizeof(_drag_innov_var)); } // calculate optical flow gyro bias errors void Ekf::calcOptFlowBias() { // accumulate the bias corrected delta angles from the navigation sensor and lapsed time _imu_del_ang_of += _imu_sample_delayed.delta_ang; _delta_time_of += _imu_sample_delayed.delta_ang_dt; // reset the accumulators if the time interval is too large if (_delta_time_of > 1.0f) { _imu_del_ang_of.setZero(); _delta_time_of = 0.0f; } // if accumulation time differences are not excessive and accumulation time is adequate // compare the optical flow and and navigation rate data and calculate a bias error if ((fabsf(_delta_time_of - _flow_sample_delayed.dt) < 0.05f) && (_delta_time_of > 0.01f) && (_flow_sample_delayed.dt > 0.01f)) { // calculate a reference angular rate Vector3f reference_body_rate; reference_body_rate = _imu_del_ang_of * (1.0f / _delta_time_of); // calculate the optical flow sensor measured body rate Vector3f of_body_rate; of_body_rate = _flow_sample_delayed.gyroXYZ * (1.0f / _flow_sample_delayed.dt); // calculate the bias estimate using a combined LPF and spike filter _flow_gyro_bias(0) = 0.99f * _flow_gyro_bias(0) + 0.01f * math::constrain((of_body_rate(0) - reference_body_rate(0)), -0.1f, 0.1f); _flow_gyro_bias(1) = 0.99f * _flow_gyro_bias(1) + 0.01f * math::constrain((of_body_rate(1) - reference_body_rate(1)), -0.1f, 0.1f); _flow_gyro_bias(2) = 0.99f * _flow_gyro_bias(2) + 0.01f * math::constrain((of_body_rate(2) - reference_body_rate(2)), -0.1f, 0.1f); } // reset the accumulators _imu_del_ang_of.setZero(); _delta_time_of = 0.0f; } // calculate the measurement variance for the optical flow sensor (rad/sec)^2 float Ekf::calcOptFlowMeasVar() { // calculate the observation noise variance - scaling noise linearly across flow quality range float R_LOS_best = fmaxf(_params.flow_noise, 0.05f); float R_LOS_worst = fmaxf(_params.flow_noise_qual_min, 0.05f); // calculate a weighting that varies between 1 when flow quality is best and 0 when flow quality is worst float weighting = (255.0f - (float)_params.flow_qual_min); if (weighting >= 1.0f) { weighting = math::constrain(((float)_flow_sample_delayed.quality - (float)_params.flow_qual_min) / weighting, 0.0f, 1.0f); } else { weighting = 0.0f; } // take the weighted average of the observation noie for the best and wort flow quality float R_LOS = sq(R_LOS_best * weighting + R_LOS_worst * (1.0f - weighting)); return R_LOS; }