forked from Archive/PX4-Autopilot
ekf2: terrain flow - migrate to Symforce
This commit is contained in:
bresch
Daniel Agar
parent
3f842f01a0
commit
b4b48cae75
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@ -0,0 +1,91 @@
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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"""
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Copyright (c) 2023 PX4 Development Team
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Redistribution and use in source and binary forms, with or without
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modification, are permitted provided that the following conditions
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are met:
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1. Redistributions of source code must retain the above copyright
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notice, this list of conditions and the following disclaimer.
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2. Redistributions in binary form must reproduce the above copyright
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notice, this list of conditions and the following disclaimer in
|
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the documentation and/or other materials provided with the
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distribution.
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3. Neither the name PX4 nor the names of its contributors may be
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used to endorse or promote products derived from this software
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without specific prior written permission.
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THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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"AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
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FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
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COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
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INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
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BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS
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OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED
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AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
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LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
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ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
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POSSIBILITY OF SUCH DAMAGE.
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File: derivation_terrain_estimator.py
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Description:
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"""
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import symforce.symbolic as sf
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from derivation_utils import *
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def predict_opt_flow(
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terrain_vpos: sf.Scalar,
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q_att: sf.V4,
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v: sf.V3,
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pos_z: sf.Scalar,
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epsilon : sf.Scalar
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):
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R_to_earth = quat_to_rot(q_att)
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flow_pred = sf.V2()
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dist = - (pos_z - terrain_vpos)
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dist = add_epsilon_sign(dist, dist, epsilon)
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flow_pred[0] = -v[1] / dist * R_to_earth[2, 2]
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flow_pred[1] = v[0] / dist * R_to_earth[2, 2]
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return flow_pred
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def terr_est_compute_flow_xy_innov_var_and_hx(
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terrain_vpos: sf.Scalar,
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P: sf.Scalar,
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q_att: sf.V4,
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v: sf.V3,
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pos_z: sf.Scalar,
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R: sf.Scalar,
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epsilon : sf.Scalar
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):
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flow_pred = predict_opt_flow(terrain_vpos, q_att, v, pos_z, epsilon)
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Hx = sf.V1(flow_pred[0]).jacobian(terrain_vpos)
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Hy = sf.V1(flow_pred[1]).jacobian(terrain_vpos)
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innov_var = sf.V2()
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innov_var[0] = (Hx * P * Hx.T + R)[0,0]
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innov_var[1] = (Hy * P * Hy.T + R)[0,0]
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return (innov_var, Hx[0, 0])
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def terr_est_compute_flow_y_innov_var_and_h(
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terrain_vpos: sf.Scalar,
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P: sf.Scalar,
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q_att: sf.V4,
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v: sf.V3,
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pos_z: sf.Scalar,
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R: sf.Scalar,
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epsilon : sf.Scalar
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):
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flow_pred = predict_opt_flow(terrain_vpos, q_att, v, pos_z, epsilon)
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Hy = sf.V1(flow_pred[1]).jacobian(terrain_vpos)
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innov_var = (Hy * P * Hy.T + R)[0,0]
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return (innov_var, Hy[0, 0])
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print("Derive terrain estimator equations...")
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generate_px4_function(terr_est_compute_flow_xy_innov_var_and_hx, output_names=["innov_var", "H"])
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generate_px4_function(terr_est_compute_flow_y_innov_var_and_h, output_names=["innov_var", "H"])
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@ -0,0 +1,66 @@
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// -----------------------------------------------------------------------------
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// This file was autogenerated by symforce from template:
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// backends/cpp/templates/function/FUNCTION.h.jinja
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// Do NOT modify by hand.
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// -----------------------------------------------------------------------------
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#pragma once
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#include <matrix/math.hpp>
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namespace sym {
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/**
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* This function was autogenerated from a symbolic function. Do not modify by hand.
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*
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* Symbolic function: terr_est_compute_flow_xy_innov_var_and_hx
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*
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* Args:
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* terrain_vpos: Scalar
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* P: Scalar
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* q_att: Matrix41
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* v: Matrix31
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* pos_z: Scalar
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* R: Scalar
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* epsilon: Scalar
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*
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* Outputs:
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* innov_var: Matrix21
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* H: Scalar
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*/
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template <typename Scalar>
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void TerrEstComputeFlowXyInnovVarAndHx(const Scalar terrain_vpos, const Scalar P,
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const matrix::Matrix<Scalar, 4, 1>& q_att,
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const matrix::Matrix<Scalar, 3, 1>& v, const Scalar pos_z,
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const Scalar R, const Scalar epsilon,
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matrix::Matrix<Scalar, 2, 1>* const innov_var = nullptr,
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Scalar* const H = nullptr) {
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// Total ops: 28
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// Input arrays
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// Intermediate terms (4)
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const Scalar _tmp0 = std::pow(q_att(0, 0), Scalar(2)) - std::pow(q_att(1, 0), Scalar(2)) -
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std::pow(q_att(2, 0), Scalar(2)) + std::pow(q_att(3, 0), Scalar(2));
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const Scalar _tmp1 = pos_z - terrain_vpos;
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const Scalar _tmp2 =
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-_tmp1 + epsilon * (2 * math::min<Scalar>(0, -(((_tmp1) > 0) - ((_tmp1) < 0))) + 1);
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const Scalar _tmp3 = P * std::pow(_tmp0, Scalar(2)) / std::pow(_tmp2, Scalar(4));
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// Output terms (2)
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if (innov_var != nullptr) {
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matrix::Matrix<Scalar, 2, 1>& _innov_var = (*innov_var);
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_innov_var(0, 0) = R + _tmp3 * std::pow(v(1, 0), Scalar(2));
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_innov_var(1, 0) = R + _tmp3 * std::pow(v(0, 0), Scalar(2));
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}
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if (H != nullptr) {
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Scalar& _H = (*H);
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_H = _tmp0 * v(1, 0) / std::pow(_tmp2, Scalar(2));
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}
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} // NOLINT(readability/fn_size)
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// NOLINTNEXTLINE(readability/fn_size)
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} // namespace sym
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@ -0,0 +1,65 @@
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// -----------------------------------------------------------------------------
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// This file was autogenerated by symforce from template:
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// backends/cpp/templates/function/FUNCTION.h.jinja
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// Do NOT modify by hand.
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// -----------------------------------------------------------------------------
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#pragma once
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#include <matrix/math.hpp>
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namespace sym {
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/**
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* This function was autogenerated from a symbolic function. Do not modify by hand.
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*
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* Symbolic function: terr_est_compute_flow_y_innov_var_and_h
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*
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* Args:
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* terrain_vpos: Scalar
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* P: Scalar
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* q_att: Matrix41
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* v: Matrix31
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* pos_z: Scalar
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* R: Scalar
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* epsilon: Scalar
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*
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* Outputs:
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* innov_var: Scalar
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* H: Scalar
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*/
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template <typename Scalar>
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void TerrEstComputeFlowYInnovVarAndH(const Scalar terrain_vpos, const Scalar P,
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const matrix::Matrix<Scalar, 4, 1>& q_att,
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const matrix::Matrix<Scalar, 3, 1>& v, const Scalar pos_z,
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const Scalar R, const Scalar epsilon,
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Scalar* const innov_var = nullptr, Scalar* const H = nullptr) {
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// Total ops: 26
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// Input arrays
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// Intermediate terms (3)
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const Scalar _tmp0 = std::pow(q_att(0, 0), Scalar(2)) - std::pow(q_att(1, 0), Scalar(2)) -
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std::pow(q_att(2, 0), Scalar(2)) + std::pow(q_att(3, 0), Scalar(2));
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const Scalar _tmp1 = pos_z - terrain_vpos;
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const Scalar _tmp2 =
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-_tmp1 + epsilon * (2 * math::min<Scalar>(0, -(((_tmp1) > 0) - ((_tmp1) < 0))) + 1);
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// Output terms (2)
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if (innov_var != nullptr) {
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Scalar& _innov_var = (*innov_var);
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_innov_var =
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P * std::pow(_tmp0, Scalar(2)) * std::pow(v(0, 0), Scalar(2)) / std::pow(_tmp2, Scalar(4)) +
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R;
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}
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if (H != nullptr) {
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Scalar& _H = (*H);
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_H = -_tmp0 * v(0, 0) / std::pow(_tmp2, Scalar(2));
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}
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} // NOLINT(readability/fn_size)
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// NOLINTNEXTLINE(readability/fn_size)
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} // namespace sym
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@ -1,97 +0,0 @@
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"""
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This script calculates the observation scalars (H matrix) for fusing optical flow
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measurements for terrain estimation.
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@author: roman
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"""
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from sympy import *
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# q: quaternion describing rotation from frame 1 to frame 2
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# returns a rotation matrix derived form q which describes the same
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# rotation
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def quat2Rot(q):
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q0 = q[0]
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q1 = q[1]
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q2 = q[2]
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q3 = q[3]
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Rot = Matrix([[q0**2 + q1**2 - q2**2 - q3**2, 2*(q1*q2 - q0*q3), 2*(q1*q3 + q0*q2)],
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[2*(q1*q2 + q0*q3), q0**2 - q1**2 + q2**2 - q3**2, 2*(q2*q3 - q0*q1)],
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[2*(q1*q3-q0*q2), 2*(q2*q3 + q0*q1), q0**2 - q1**2 - q2**2 + q3**2]])
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return Rot
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# take an expression calculated by the cse() method and write the expression
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# into a text file in C format
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def write_simplified(P_touple, filename, out_name):
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subs = P_touple[0]
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P = Matrix(P_touple[1])
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fd = open(filename, 'a')
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is_vector = P.shape[0] == 1 or P.shape[1] == 1
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# write sub expressions
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for index, item in enumerate(subs):
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fd.write('float ' + str(item[0]) + ' = ' + str(item[1]) + ';\n')
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# write actual matrix values
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fd.write('\n')
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if not is_vector:
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iterator = range(0,sqrt(len(P)), 1)
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for row in iterator:
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for column in iterator:
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fd.write(out_name + '(' + str(row) + ',' + str(column) + ') = ' + str(P[row, column]) + ';\n')
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else:
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iterator = range(0, len(P), 1)
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for item in iterator:
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fd.write(out_name + '(' + str(item) + ') = ' + str(P[item]) + ';\n')
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fd.write('\n\n')
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fd.close()
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########## Symbolic variable definition #######################################
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# vehicle velocity
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v_x = Symbol("v_x", real=True) # vehicle body x velocity
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v_y = Symbol("v_y", real=True) # vehicle body y velocity
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# unit quaternion describing vehicle attitude, qw is real part
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qw = Symbol("q0", real=True)
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qx = Symbol("q1", real=True)
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qy = Symbol("q2", real=True)
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qz = Symbol("q3", real=True)
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q_att = Matrix([qw, qx, qy, qz])
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# terrain vertial position in local NED frame
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_terrain_vpos = Symbol("_terrain_vpos", real=True)
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_terrain_var = Symbol("_terrain_var", real=True)
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# vehicle vertical position in local NED frame
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pos_z = Symbol("z", real=True)
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R_body_to_earth = quat2Rot(q_att)
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# Optical flow around x axis
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flow_x = -v_y / (_terrain_vpos - pos_z) * R_body_to_earth[2,2]
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# Calculate observation scalar
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H_x = Matrix([flow_x]).jacobian(Matrix([_terrain_vpos]))
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H_x_simple = cse(H_x, symbols('t0:30'))
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# Optical flow around y axis
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flow_y = v_x / (_terrain_vpos - pos_z) * R_body_to_earth[2,2]
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# Calculate observation scalar
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H_y = Matrix([flow_y]).jacobian(Matrix([_terrain_vpos]))
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H_y_simple = cse(H_y, symbols('t0:30'))
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write_simplified(H_x_simple, "flow_x_observation.txt", "Hx")
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write_simplified(H_y_simple, "flow_y_observation.txt", "Hy")
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@ -1,6 +1,6 @@
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/****************************************************************************
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*
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* Copyright (c) 2015 Estimation and Control Library (ECL). All rights reserved.
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* Copyright (c) 2015-2023 PX4 Development Team. All rights reserved.
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*
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* Redistribution and use in source and binary forms, with or without
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* modification, are permitted provided that the following conditions
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@ -12,7 +12,7 @@
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* notice, this list of conditions and the following disclaimer in
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* the documentation and/or other materials provided with the
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* distribution.
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* 3. Neither the name ECL nor the names of its contributors may be
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* 3. Neither the name PX4 nor the names of its contributors may be
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* used to endorse or promote products derived from this software
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* without specific prior written permission.
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*
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@ -33,13 +33,13 @@
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/**
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* @file terrain_estimator.cpp
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* Function for fusing rangefinder measurements to estimate terrain vertical position/
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*
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* @author Paul Riseborough <p_riseborough@live.com.au>
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*
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* Function for fusing rangefinder and optical flow measurements
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* to estimate terrain vertical position
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*/
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#include "ekf.h"
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#include "python/ekf_derivation/generated/terr_est_compute_flow_xy_innov_var_and_hx.h"
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#include "python/ekf_derivation/generated/terr_est_compute_flow_y_innov_var_and_h.h"
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#include <mathlib/mathlib.h>
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@ -254,6 +254,8 @@ void Ekf::controlHaglFlowFusion()
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}
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if (_flow_data_ready) {
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updateOptFlow(_aid_src_optical_flow);
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const bool continuing_conditions_passing = _control_status.flags.in_air
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&& !_control_status.flags.opt_flow
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&& _control_status.flags.gps
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@ -320,90 +322,71 @@ void Ekf::fuseFlowForTerrain()
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{
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_aid_src_optical_flow.fusion_enabled = true;
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// calculate optical LOS rates using optical flow rates that have had the body angular rate contribution removed
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// correct for gyro bias errors in the data used to do the motion compensation
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// Note the sign convention used: A positive LOS rate is a RH rotation of the scene about that axis.
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const Vector2f opt_flow_rate = _flow_compensated_XY_rad / _flow_sample_delayed.dt;
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const float R_LOS = _aid_src_optical_flow.observation_variance[0];
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// get latest estimated orientation
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const float q0 = _state.quat_nominal(0);
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const float q1 = _state.quat_nominal(1);
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const float q2 = _state.quat_nominal(2);
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const float q3 = _state.quat_nominal(3);
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// calculate the height above the ground of the optical flow camera. Since earth frame is NED
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// a positive offset in earth frame leads to a smaller height above the ground.
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float range = predictFlowRange();
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// calculate the optical flow observation variance
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const float R_LOS = calcOptFlowMeasVar(_flow_sample_delayed);
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const float state = _terrain_vpos; // linearize both axes using the same state value
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Vector2f innov_var;
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float H;
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sym::TerrEstComputeFlowXyInnovVarAndHx(state, _terrain_var, _state.quat_nominal, _state.vel, _state.pos(2), R_LOS, FLT_EPSILON, &innov_var, &H);
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innov_var.copyTo(_aid_src_optical_flow.innovation_variance);
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// get rotation matrix from earth to body
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const Dcmf earth_to_body = quatToInverseRotMat(_state.quat_nominal);
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// calculate the sensor position relative to the IMU
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const Vector3f pos_offset_body = _params.flow_pos_body - _params.imu_pos_body;
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// calculate the velocity of the sensor relative to the imu in body frame
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// Note: _flow_sample_delayed.gyro_xyz is the negative of the body angular velocity, thus use minus sign
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const Vector3f vel_rel_imu_body = Vector3f(-_flow_sample_delayed.gyro_xyz / _flow_sample_delayed.dt) % pos_offset_body;
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// calculate the velocity of the sensor in the earth frame
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const Vector3f vel_rel_earth = _state.vel + _R_to_earth * vel_rel_imu_body;
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// rotate into body frame
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const Vector3f vel_body = earth_to_body * vel_rel_earth;
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// constrain terrain to minimum allowed value and predict height above ground
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_terrain_vpos = fmaxf(_terrain_vpos, _params.rng_gnd_clearance + _state.pos(2));
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const float pred_hagl_inv = 1.f / (_terrain_vpos - _state.pos(2));
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// calculate prediced optical flow
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const float pred_flow_x = vel_body(1) * earth_to_body(2, 2) * pred_hagl_inv;
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const float pred_flow_y = -vel_body(0) * earth_to_body(2, 2) * pred_hagl_inv;
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// calculate flow innovation
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_aid_src_optical_flow.innovation[0] = pred_flow_x - opt_flow_rate(0);
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_aid_src_optical_flow.innovation[1] = pred_flow_y - opt_flow_rate(1);
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||||
|
||||
const float t0 = q0 * q0 - q1 * q1 - q2 * q2 + q3 * q3;
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// Calculate observation matrix for flow around
|
||||
const float Hx = vel_body(1) * t0 * pred_hagl_inv * pred_hagl_inv;
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const float Hy = -vel_body(0) * t0 * pred_hagl_inv * pred_hagl_inv;
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||||
|
||||
// Constrain terrain variance to be non-negative
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||||
_terrain_var = fmaxf(_terrain_var, sq(0.01f));
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||||
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||||
// Cacluate innovation variance
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_aid_src_optical_flow.innovation_variance[0] = Hx * Hx * _terrain_var + R_LOS;
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||||
_aid_src_optical_flow.innovation_variance[1] = Hy * Hy * _terrain_var + R_LOS;
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||||
if ((_aid_src_optical_flow.innovation_variance[0] < R_LOS)
|
||||
|| (_aid_src_optical_flow.innovation_variance[1] < R_LOS)) {
|
||||
// we need to reinitialise the covariance matrix and abort this fusion step
|
||||
ECL_ERR("Opt flow error - covariance reset");
|
||||
_terrain_var = 100.0f;
|
||||
return;
|
||||
}
|
||||
|
||||
// run the innovation consistency check and record result
|
||||
setEstimatorAidStatusTestRatio(_aid_src_optical_flow, math::max(_params.flow_innov_gate, 1.f));
|
||||
|
||||
// do not perform measurement update if badly conditioned
|
||||
_innov_check_fail_status.flags.reject_optflow_X = (_aid_src_optical_flow.test_ratio[0] > 1.f);
|
||||
_innov_check_fail_status.flags.reject_optflow_Y = (_aid_src_optical_flow.test_ratio[1] > 1.f);
|
||||
|
||||
// if either axis fails we abort the fusion
|
||||
if (_aid_src_optical_flow.innovation_rejected) {
|
||||
return;
|
||||
}
|
||||
|
||||
// calculate the kalman gain for the flow measurement
|
||||
const float Kx = _terrain_var * Hx / _aid_src_optical_flow.innovation_variance[0];
|
||||
// fuse observation axes sequentially
|
||||
for (uint8_t index = 0; index <= 1; index++) {
|
||||
if (index == 0) {
|
||||
// everything was already computed above
|
||||
|
||||
// calculate correction term for terrain variance
|
||||
const float KxHxP = Kx * Hx * _terrain_var;
|
||||
} else if (index == 1) {
|
||||
// recalculate innovation variance because state covariances have changed due to previous fusion (linearise using the same initial state for all axes)
|
||||
sym::TerrEstComputeFlowYInnovVarAndH(state, _terrain_var, _state.quat_nominal, _state.vel, _state.pos(2), R_LOS, FLT_EPSILON, &_aid_src_optical_flow.innovation_variance[1], &H);
|
||||
|
||||
_terrain_vpos += Kx * _aid_src_optical_flow.innovation[0];
|
||||
// guard against negative variance
|
||||
_terrain_var = fmaxf(_terrain_var - KxHxP, sq(0.01f));
|
||||
// recalculate the innovation using the updated state
|
||||
const Vector2f vel_body = predictFlowVelBody();
|
||||
range = predictFlowRange();
|
||||
_aid_src_optical_flow.innovation[1] = (-vel_body(0) / range) - _aid_src_optical_flow.observation[1];
|
||||
|
||||
if (_aid_src_optical_flow.innovation_variance[1] < R_LOS) {
|
||||
// we need to reinitialise the covariance matrix and abort this fusion step
|
||||
ECL_ERR("Opt flow error - covariance reset");
|
||||
_terrain_var = 100.0f;
|
||||
return;
|
||||
}
|
||||
}
|
||||
|
||||
// calculate the kalman gain for the flow measurement
|
||||
const float Ky = _terrain_var * Hy / _aid_src_optical_flow.innovation_variance[1];
|
||||
float Kfusion = _terrain_var * H / _aid_src_optical_flow.innovation_variance[index];
|
||||
|
||||
// calculate correction term for terrain variance
|
||||
const float KyHyP = Ky * Hy * _terrain_var;
|
||||
_terrain_vpos += Kfusion * _aid_src_optical_flow.innovation[0];
|
||||
// constrain terrain to minimum allowed value and predict height above ground
|
||||
_terrain_vpos = fmaxf(_terrain_vpos, _params.rng_gnd_clearance + _state.pos(2));
|
||||
|
||||
_terrain_vpos += Ky * _aid_src_optical_flow.innovation_variance[1];
|
||||
// guard against negative variance
|
||||
_terrain_var = fmaxf(_terrain_var - KyHyP, sq(0.01f));
|
||||
// guard against negative variance
|
||||
_terrain_var = fmaxf(_terrain_var - Kfusion * H * _terrain_var, sq(0.01f));
|
||||
}
|
||||
|
||||
_fault_status.flags.bad_optflow_X = false;
|
||||
_fault_status.flags.bad_optflow_Y = false;
|
||||
|
||||
_time_last_flow_terrain_fuse = _time_delayed_us;
|
||||
//_aid_src_optical_flow.time_last_fuse = _time_delayed_us; // TODO: separate aid source status for OF terrain?
|
||||
|
|
|
|||
Loading…
Reference in New Issue