wrong drone's dynamics model for estimation used in the first icra submission
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/*
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* DroneDynamicsFactor.h
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*
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* Created on: Oct 1, 2014
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* Author: krunal
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*/
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#pragma once
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#include <boost/lexical_cast.hpp>
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#include <gtsam/geometry/Pose3.h>
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#include <gtsam/geometry/Point3.h>
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#include <gtsam/base/LieVector.h>
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#include <gtsam/nonlinear/NonlinearFactor.h>
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namespace gtsam {
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/**
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* Binary factor for a range measurement
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* @addtogroup SLAM
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*/
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class DroneDynamicsFactor: public NoiseModelFactor2<Pose3, LieVector> {
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private:
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LieVector measured_; /** body velocity measured from raw acc and motor inputs*/
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typedef DroneDynamicsFactor This;
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typedef NoiseModelFactor2<Pose3, LieVector> Base;
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public:
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DroneDynamicsFactor() {} /* Default constructor */
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DroneDynamicsFactor(Key poseKey, Key velKey, const LieVector& measured,
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const SharedNoiseModel& model) :
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Base(model, poseKey, velKey), measured_(measured) {
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}
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virtual ~DroneDynamicsFactor() {}
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/// @return a deep copy of this factor
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virtual gtsam::NonlinearFactor::shared_ptr clone() const {
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return boost::static_pointer_cast<gtsam::NonlinearFactor>(
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gtsam::NonlinearFactor::shared_ptr(new This(*this))); }
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/** h(x)-z */
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Vector evaluateError(const Pose3& pose, const LieVector& vel,
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boost::optional<Matrix&> H1 = boost::none, boost::optional<Matrix&> H2 = boost::none) const {
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// error = v - wRb*measured
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Rot3 wRb = pose.rotation();
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Vector3 error;
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if (H1 || H2) {
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*H2 = eye(3);
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*H1 = zeros(3,6);
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Matrix H1Rot;
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error = wRb.unrotate(Point3(vel.vector()), H1Rot, H2).vector() - measured_.vector();
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(*H1).block(0,0,3,3) = H1Rot;
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}
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else {
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error = wRb.unrotate(Point3(vel.vector())).vector() - measured_.vector();
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}
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return error;
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}
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/** return the measured */
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LieVector measured() const {
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return measured_;
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}
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/** equals specialized to this factor */
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virtual bool equals(const NonlinearFactor& expected, double tol=1e-9) const {
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const This *e = dynamic_cast<const This*> (&expected);
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return e != NULL
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&& Base::equals(*e, tol)
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;
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}
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/** print contents */
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void print(const std::string& s="", const KeyFormatter& keyFormatter = DefaultKeyFormatter) const {
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std::cout << s << "DroneDynamicsFactor, measured = " << measured_.vector().transpose() << std::endl;
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Base::print("", keyFormatter);
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}
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private:
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/** Serialization function */
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friend class boost::serialization::access;
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template<class ARCHIVE>
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void serialize(ARCHIVE & ar, const unsigned int version) {
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ar & boost::serialization::make_nvp("NoiseModelFactor2",
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boost::serialization::base_object<Base>(*this));
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ar & BOOST_SERIALIZATION_NVP(measured_);
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}
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}; // DroneDynamicsFactor
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} // namespace gtsam
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/*
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* DroneDynamicsVelXYFactor.h
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*
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* Created on: Oct 1, 2014
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* Author: krunal
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*/
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#pragma once
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#include <boost/lexical_cast.hpp>
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#include <gtsam/geometry/Pose3.h>
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#include <gtsam/geometry/Point3.h>
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#include <gtsam/base/LieVector.h>
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#include <gtsam/nonlinear/NonlinearFactor.h>
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namespace gtsam {
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/**
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* Binary factor for a range measurement
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* @addtogroup SLAM
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*/
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class DroneDynamicsVelXYFactor: public NoiseModelFactor3<Pose3, LieVector, LieVector> {
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private:
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Vector motors_; /** motor inputs */
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Vector acc_; /** raw acc */
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Matrix M_;
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typedef DroneDynamicsVelXYFactor This;
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typedef NoiseModelFactor3<Pose3, LieVector, LieVector> Base;
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public:
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DroneDynamicsVelXYFactor() {} /* Default constructor */
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DroneDynamicsVelXYFactor(Key poseKey, Key velKey, Key cKey, const Vector& motors, const Vector& acc,
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const SharedNoiseModel& model) :
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Base(model, poseKey, velKey, cKey), motors_(motors), acc_(acc), M_(computeM(motors, acc)) {
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}
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virtual ~DroneDynamicsVelXYFactor() {}
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/// @return a deep copy of this factor
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virtual gtsam::NonlinearFactor::shared_ptr clone() const {
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return boost::static_pointer_cast<gtsam::NonlinearFactor>(
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gtsam::NonlinearFactor::shared_ptr(new This(*this))); }
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// M = [sum(sqrt(m))ax 1 0 0; 0 0 sum(sqrt(m))ay 1; 0 0 0 0]
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Matrix computeM(const Vector& motors, const Vector& acc) const {
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Matrix M = zeros(3,4);
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double sqrtSumMotors = sqrt(motors(0)) + sqrt(motors(1)) + sqrt(motors(2)) + sqrt(motors(3));
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M(0,0) = sqrtSumMotors*acc(0); M(0, 1) = 1.0;
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M(1,2) = 1.0; M(1, 3) = sqrtSumMotors*acc(1);
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return M;
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}
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/** h(x)-z */
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Vector evaluateError(const Pose3& pose, const LieVector& vel, const LieVector& c,
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boost::optional<Matrix&> H1 = boost::none, boost::optional<Matrix&> H2 = boost::none,
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boost::optional<Matrix&> H3 = boost::none) const {
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// error = R'*v - M*c, where
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Rot3 wRb = pose.rotation();
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Vector error;
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if (H1 || H2 || H3) {
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*H1 = zeros(3, 6);
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*H2 = eye(3);
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Matrix H1Rot;
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error = wRb.unrotate(Point3(vel.vector()), H1Rot, H2).vector() - M_*c.vector();
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(*H1).block(0,0,3,3) = H1Rot;
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*H3 = -M_;
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}
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else {
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error = wRb.unrotate(Point3(vel.vector())).vector() - M_*c.vector();
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}
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return error;
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}
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/** equals specialized to this factor */
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virtual bool equals(const NonlinearFactor& expected, double tol=1e-9) const {
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const This *e = dynamic_cast<const This*> (&expected);
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return e != NULL
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&& Base::equals(*e, tol)
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;
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}
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/** print contents */
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void print(const std::string& s="", const KeyFormatter& keyFormatter = DefaultKeyFormatter) const {
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std::cout << s << "DroneDynamicsVelXYFactor, motors = " << motors_.transpose() << " acc: " << acc_.transpose() << std::endl;
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Base::print("", keyFormatter);
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}
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private:
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/** Serialization function */
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friend class boost::serialization::access;
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template<class ARCHIVE>
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void serialize(ARCHIVE & ar, const unsigned int version) {
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ar & boost::serialization::make_nvp("NoiseModelFactor2",
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boost::serialization::base_object<Base>(*this));
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ar & BOOST_SERIALIZATION_NVP(motors_);
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ar & BOOST_SERIALIZATION_NVP(acc_);
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}
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}; // DroneDynamicsVelXYFactor
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} // namespace gtsam
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file testRangeFactor.cpp
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* @brief Unit tests for DroneDynamicsFactor Class
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* @author Stephen Williams
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* @date Oct 2012
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*/
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#include <CppUnitLite/TestHarness.h>
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#include <gtsam/slam/DroneDynamicsFactor.h>
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#include <gtsam/base/numericalDerivative.h>
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#include <gtsam/base/TestableAssertions.h>
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#include <boost/bind.hpp>
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using namespace std;
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using namespace gtsam;
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// Create a noise model for the pixel error
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static SharedNoiseModel model(noiseModel::Unit::Create(3));
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/* ************************************************************************* */
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LieVector factorError(const Pose3& pose, const LieVector& vel, const DroneDynamicsFactor& factor) {
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return factor.evaluateError(pose, vel);
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}
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/* ************************************************************************* */
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TEST( DroneDynamicsFactor, Error) {
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// Create a factor
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Key poseKey(1);
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Key velKey(2);
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LieVector measurement((Vector(3)<<10.0, 1.5, 0.0));
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DroneDynamicsFactor factor(poseKey, velKey, measurement, model);
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// Set the linearization point
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Pose3 pose(Rot3::ypr(1.0, 2.0, 0.57), Point3());
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LieVector vel((Vector(3) <<
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-2.913425624770731,
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-2.200086236883632,
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-9.429823523226959));
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// Use the factor to calculate the error
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Matrix H1, H2;
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Vector actualError(factor.evaluateError(pose, vel, H1, H2));
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Vector expectedError = zero(3);
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// Verify we get the expected error
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CHECK(assert_equal(expectedError, actualError, 1e-9));
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// Use numerical derivatives to calculate the Jacobians
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Matrix H1Expected, H2Expected;
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H1Expected = numericalDerivative11<LieVector, Pose3>(boost::bind(&factorError, _1, vel, factor), pose);
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H2Expected = numericalDerivative11<LieVector, LieVector>(boost::bind(&factorError, pose, _1, factor), vel);
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// Verify the Jacobians are correct
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CHECK(assert_equal(H1Expected, H1, 1e-9));
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CHECK(assert_equal(H2Expected, H2, 1e-9));
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}
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/* *************************************************************************
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TEST( DroneDynamicsFactor, Jacobian2D ) {
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// Create a factor
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Key poseKey(1);
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Key pointKey(2);
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double measurement(10.0);
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RangeFactor2D factor(poseKey, pointKey, measurement, model);
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// Set the linearization point
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Pose2 pose(1.0, 2.0, 0.57);
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Point2 point(-4.0, 11.0);
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// Use the factor to calculate the Jacobians
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Matrix H1Actual, H2Actual;
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factor.evaluateError(pose, point, H1Actual, H2Actual);
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// Use numerical derivatives to calculate the Jacobians
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Matrix H1Expected, H2Expected;
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H1Expected = numericalDerivative11<LieVector, Pose2>(boost::bind(&factorError2D, _1, point, factor), pose);
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H2Expected = numericalDerivative11<LieVector, Point2>(boost::bind(&factorError2D, pose, _1, factor), point);
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// Verify the Jacobians are correct
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CHECK(assert_equal(H1Expected, H1Actual, 1e-9));
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CHECK(assert_equal(H2Expected, H2Actual, 1e-9));
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}
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/* *************************************************************************
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/* ************************************************************************* */
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int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
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/* ************************************************************************* */
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/* ----------------------------------------------------------------------------
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* GTSAM Copyright 2010, Georgia Tech Research Corporation,
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* Atlanta, Georgia 30332-0415
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* All Rights Reserved
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* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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* See LICENSE for the license information
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* -------------------------------------------------------------------------- */
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/**
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* @file testRangeFactor.cpp
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* @brief Unit tests for DroneDynamicsVelXYFactor Class
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* @author Stephen Williams
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* @date Oct 2012
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*/
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#include <CppUnitLite/TestHarness.h>
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#include <gtsam/slam/DroneDynamicsVelXYFactor.h>
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#include <gtsam/base/numericalDerivative.h>
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#include <gtsam/base/TestableAssertions.h>
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#include <boost/bind.hpp>
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using namespace std;
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using namespace gtsam;
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// Create a noise model for the pixel error
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static SharedNoiseModel model(noiseModel::Unit::Create(3));
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/* ************************************************************************* */
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LieVector factorError(const Pose3& pose, const LieVector& vel, const LieVector& coeffs, const DroneDynamicsVelXYFactor& factor) {
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return factor.evaluateError(pose, vel, coeffs);
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}
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/* ************************************************************************* */
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TEST( DroneDynamicsVelXYFactor, Error) {
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// Create a factor
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Key poseKey(1);
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Key velKey(2);
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Key coeffsKey(3);
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Vector motors = (Vector(4) << 179, 180, 167, 168)/256.0;
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Vector3 acc = (Vector(3) << 2., 1., 3.);
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DroneDynamicsVelXYFactor factor(poseKey, velKey, coeffsKey, motors, acc, model);
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// Set the linearization point
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Pose3 pose(Rot3::ypr(1.0, 2.0, 0.57), Point3());
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LieVector vel((Vector(3) <<
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-2.913425624770731,
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-2.200086236883632,
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-9.429823523226959));
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LieVector coeffs((Vector(4) << -9.3, 2.7, -6.5, 1.2));
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// Use the factor to calculate the error
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Matrix H1, H2, H3;
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Vector actualError(factor.evaluateError(pose, vel, coeffs, H1, H2, H3));
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Vector expectedError = zero(3);
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// Verify we get the expected error
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// CHECK(assert_equal(expectedError, actualError, 1e-9));
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// Use numerical derivatives to calculate the Jacobians
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Matrix H1Expected, H2Expected, H3Expected;
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H1Expected = numericalDerivative11<LieVector, Pose3>(boost::bind(&factorError, _1, vel, coeffs, factor), pose);
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H2Expected = numericalDerivative11<LieVector, LieVector>(boost::bind(&factorError, pose, _1, coeffs, factor), vel);
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H3Expected = numericalDerivative11<LieVector, LieVector>(boost::bind(&factorError, pose, vel, _1, factor), coeffs);
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// Verify the Jacobians are correct
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CHECK(assert_equal(H1Expected, H1, 1e-9));
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CHECK(assert_equal(H2Expected, H2, 1e-9));
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CHECK(assert_equal(H3Expected, H3, 1e-9));
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}
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/* *************************************************************************
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TEST( DroneDynamicsVelXYFactor, Jacobian2D ) {
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// Create a factor
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Key poseKey(1);
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Key pointKey(2);
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double measurement(10.0);
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RangeFactor2D factor(poseKey, pointKey, measurement, model);
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// Set the linearization point
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Pose2 pose(1.0, 2.0, 0.57);
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Point2 point(-4.0, 11.0);
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// Use the factor to calculate the Jacobians
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Matrix H1Actual, H2Actual;
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factor.evaluateError(pose, point, H1Actual, H2Actual);
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// Use numerical derivatives to calculate the Jacobians
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Matrix H1Expected, H2Expected;
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H1Expected = numericalDerivative11<LieVector, Pose2>(boost::bind(&factorError2D, _1, point, factor), pose);
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H2Expected = numericalDerivative11<LieVector, Point2>(boost::bind(&factorError2D, pose, _1, factor), point);
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// Verify the Jacobians are correct
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CHECK(assert_equal(H1Expected, H1Actual, 1e-9));
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CHECK(assert_equal(H2Expected, H2Actual, 1e-9));
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}
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/* *************************************************************************
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/* ************************************************************************* */
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int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
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/* ************************************************************************* */
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