/**************************************************************************** * * 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 #include #include void Ekf::fuseOptFlow() { float gndclearance = fmaxf(_params.rng_gnd_clearance, 0.1f); // get latest estimated orientation const float q0 = _state.quat_nominal(0); const float q1 = _state.quat_nominal(1); const float q2 = _state.quat_nominal(2); const float q3 = _state.quat_nominal(3); // get latest velocity in earth frame const float vn = _state.vel(0); const float ve = _state.vel(1); const float vd = _state.vel(2); // calculate the optical flow observation variance const float R_LOS = calcOptFlowMeasVar(); float H_LOS[2][24] = {}; // Optical flow observation Jacobians float Kfusion[24][2] = {}; // Optical flow Kalman gains // get rotation matrix from earth to body const Dcmf earth_to_body = quat_to_invrotmat(_state.quat_nominal); // calculate the sensor position relative to the IMU const Vector3f pos_offset_body = _params.flow_pos_body - _params.imu_pos_body; // calculate the velocity of the sensor relative to the imu in body frame // Note: _flow_sample_delayed.gyro_xyz is the negative of the body angular velocity, thus use minus sign const Vector3f vel_rel_imu_body = Vector3f(-_flow_sample_delayed.gyro_xyz / _flow_sample_delayed.dt) % pos_offset_body; // calculate the velocity of the sensor in the earth frame const Vector3f vel_rel_earth = _state.vel + _R_to_earth * vel_rel_imu_body; // rotate into body frame const Vector3f vel_body = earth_to_body * vel_rel_earth; // height above ground of the IMU float heightAboveGndEst = _terrain_vpos - _state.pos(2); // calculate the sensor position relative to the IMU in earth frame const Vector3f pos_offset_earth = _R_to_earth * pos_offset_body; // calculate the height above the ground of the optical flow camera. Since earth frame is NED // a positive offset in earth frame leads to a smaller height above the ground. heightAboveGndEst -= pos_offset_earth(2); // constrain minimum height above ground heightAboveGndEst = math::max(heightAboveGndEst, gndclearance); // calculate range from focal point to centre of image const 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 rotation of the scene about that axis. Vector2f opt_flow_rate; opt_flow_rate(0) = _flow_compensated_XY_rad(0) / _flow_sample_delayed.dt + _flow_gyro_bias(0); opt_flow_rate(1) = _flow_compensated_XY_rad(1) / _flow_sample_delayed.dt + _flow_gyro_bias(1); if (opt_flow_rate.norm() < _flow_max_rate) { _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 observation 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); } 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 observation 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 the innovation consistency check and record result bool flow_fail = false; float test_ratio[2]; test_ratio[0] = sq(_flow_innov[0]) / (sq(math::max(_params.flow_innov_gate, 1.0f)) * _flow_innov_var[0]); test_ratio[1] = sq(_flow_innov[1]) / (sq(math::max(_params.flow_innov_gate, 1.0f)) * _flow_innov_var[1]); _optflow_test_ratio = math::max(test_ratio[0],test_ratio[1]); for (uint8_t obs_index = 0; obs_index <= 1; obs_index++) { if (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 matrix::SquareMatrix KHP; 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 matrix 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 P.uncorrelateCovarianceSetVariance<1>(i, 0.0f); //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 corrections 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 matrix for gross errors fixCovarianceErrors(true); // apply the state corrections fuse(gain, _flow_innov[obs_index]); _time_last_of_fuse = _time_last_imu; } } } // calculate optical flow body angular rate compensation // returns false if bias corrected body rate data is unavailable bool Ekf::calcOptFlowBodyRateComp() { // 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; return false; } const bool use_flow_sensor_gyro = ISFINITE(_flow_sample_delayed.gyro_xyz(0)) && ISFINITE(_flow_sample_delayed.gyro_xyz(1)) && ISFINITE(_flow_sample_delayed.gyro_xyz(2)); if (use_flow_sensor_gyro) { // 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) < 0.1f) && (_delta_time_of > FLT_EPSILON)) { const Vector3f reference_body_rate(_imu_del_ang_of * (1.0f / _delta_time_of)); const Vector3f measured_body_rate(_flow_sample_delayed.gyro_xyz * (1.0f / _flow_sample_delayed.dt)); // calculate the bias estimate using a combined LPF and spike filter _flow_gyro_bias = _flow_gyro_bias * 0.99f + matrix::constrain(measured_body_rate - reference_body_rate, -0.1f, 0.1f) * 0.01f; } } else { // Use the EKF gyro data if optical flow sensor gyro data is not available // for clarification of the sign see definition of flowSample and imuSample in common.h _flow_sample_delayed.gyro_xyz = -_imu_del_ang_of; _flow_gyro_bias.zero(); } // reset the accumulators _imu_del_ang_of.setZero(); _delta_time_of = 0.0f; return true; } // 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 const float R_LOS_best = fmaxf(_params.flow_noise, 0.05f); const 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 noise for the best and wort flow quality const float R_LOS = sq(R_LOS_best * weighting + R_LOS_worst * (1.0f - weighting)); return R_LOS; }