addOdometry now works
Frank Dellaert
parent
c987ab397c
commit
d11d674c01
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@ -8,6 +8,8 @@
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#include "FactorGraph-inl.h"
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#include "NonlinearOptimizer-inl.h"
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#include "UrbanGraph.h"
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#include "UrbanMeasurement.h"
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#include "UrbanOdometry.h"
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namespace gtsam {
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@ -43,7 +45,12 @@ namespace gtsam {
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/* ************************************************************************* */
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void UrbanGraph::addOdometry(double dx, double yaw, double sigmadx,
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double sigmayaw, int p) {
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// TODO
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Vector z = Vector_(dx,0,0,yaw,0,0);
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Matrix cov = eye(6);
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cov(1,1)=sigmadx*sigmadx;
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cov(4,4)=sigmadx*sigmadx;
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sharedFactor factor(new UrbanOdometry(symbol('x',p),symbol('x',p+1),z,cov));
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push_back(factor);
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}
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/* ************************************************************************* */
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@ -8,7 +8,7 @@
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#pragma once
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#include "NonlinearFactorGraph.h"
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#include "UrbanMeasurement.h"
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#include "UrbanFactor.h"
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namespace gtsam {
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@ -0,0 +1,86 @@
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/**
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* @file UrbanOdometry.cpp
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* @brief A non-linear factor specialized to the Visual SLAM problem
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* @author Frank Dellaert
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* @author Viorela Ila
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*/
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#include "UrbanOdometry.h"
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using namespace std;
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namespace gtsam {
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/* ************************************************************************* *
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UrbanOdometry::UrbanOdometry() {
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/// Arbitrary values
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robotPoseNumber_ = 1;
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robotPoseName_ = symbol('x',robotPoseNumber_);
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keys_.push_back(robotPoseName_);
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}
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/* ************************************************************************* *
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UrbanOdometry::UrbanOdometry(const Point2& z, double sigma, int cn, int ln)
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: NonlinearFactor<Pose3Config>(z.vector(), sigma)
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{
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robotPoseNumber_ = cn;
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landmarkNumber_ = ln;
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robotPoseName_ = symbol('x',robotPoseNumber_);
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landmarkName_ = symbol('l',landmarkNumber_);
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keys_.push_back(robotPoseName_);
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keys_.push_back(landmarkName_);
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}
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/* ************************************************************************* *
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void UrbanOdometry::print(const std::string& s) const {
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printf("%s %s %s\n", s.c_str(), robotPoseName_.c_str(), landmarkName_.c_str());
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gtsam::print(this->z_, s+".z");
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}
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/* ************************************************************************* *
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bool UrbanOdometry::equals(const UrbanOdometry& p, double tol) const {
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if (&p == NULL) return false;
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if (robotPoseNumber_ != p.robotPoseNumber_ || landmarkNumber_ != p.landmarkNumber_) return false;
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if (!equal_with_abs_tol(this->z_,p.z_,tol)) return false;
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return true;
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}
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/* ************************************************************************* *
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// TODO Implement transformPoint2_from
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// the difference here that we have a 2d point in a 3D coordinate
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Vector UrbanOdometry::predict(const Pose3Config& c) const {
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Pose3 pose = c.cameraPose(robotPoseNumber_);
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Point2 landmark = c.landmarkPoint(landmarkNumber_);
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return transformPoint2_from(SimpleCamera(*K_,pose), landmark).vector();
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}
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/* ************************************************************************* *
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Vector UrbanOdometry::error_vector(const Pose3Config& c) const {
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// Right-hand-side b = (z - h(x))/sigma
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Point2 h = predict(c);
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return (this->z_ - h);
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}
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/* ************************************************************************* *
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GaussianFactor::shared_ptr UrbanOdometry::linearize(const Pose3Config& c) const
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{
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// get arguments from config
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Pose3 pose = c.cameraPose(robotPoseNumber_);
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Point3 landmark = c.landmarkPoint(landmarkNumber_);
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SimpleCamera camera(*K_,pose);
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// Jacobians
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Matrix Dh1 = Dproject_pose(camera, landmark);
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Matrix Dh2 = Dproject_point(camera, landmark);
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// Right-hand-side b = (z - h(x))
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Vector h = project(camera, landmark).vector();
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Vector b = this->z_ - h;
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// Make new linearfactor, divide by sigma
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GaussianFactor::shared_ptr
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p(new GaussianFactor(robotPoseName_, Dh1, landmarkName_, Dh2, b, this->sigma_));
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return p;
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}
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/* ************************************************************************* */
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} // namespace gtsam
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@ -0,0 +1,79 @@
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/**
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* @file UrbanOdometry.h
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* @brief A Nonlinear Factor, specialized for Urban application
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* @author Frank Dellaert
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* @author Viorela Ila
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*/
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#pragma once
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/*
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#include <map>
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#include "NonlinearFactor.h"
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#include "GaussianFactor.h"
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#include "VectorConfig.h"
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#include "Pose3.h"
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*/
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#include "UrbanFactor.h"
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namespace gtsam {
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class UrbanOdometry: public UrbanFactor {
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private:
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std::string key1_, key2_; /** The keys of the two poses, order matters */
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Matrix square_root_inverse_covariance_; /** sqrt(inv(measurement_covariance)) */
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public:
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typedef boost::shared_ptr<UrbanOdometry> shared_ptr; // shorthand for a smart pointer to a factor
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UrbanOdometry(const std::string& key1, const std::string& key2,
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const Vector& measured, const Matrix& measurement_covariance) :
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UrbanFactor(measured, 1.0),
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key1_(key1), key2_(key2) {
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square_root_inverse_covariance_ = inverse_square_root(
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measurement_covariance);
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}
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/** implement functions needed for Testable */
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void print(const std::string& name) const {
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std::cout << name << std::endl;
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std::cout << "Factor " << std::endl;
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std::cout << "key1 " << key1_ << std::endl;
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std::cout << "key2 " << key2_ << std::endl;
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gtsam::print(z_,"measured ");
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gtsam::print(square_root_inverse_covariance_, "MeasurementCovariance");
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}
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bool equals(const NonlinearFactor<UrbanConfig>& expected, double tol) const {
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return false;
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}
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/** implement functions needed to derive from Factor */
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Vector error_vector(const UrbanConfig& config) const {
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return zero(6);
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}
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std::list<std::string> keys() const {
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std::list<std::string> l;
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return l;
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}
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std::size_t size() const {
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return 2;
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}
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/** linearize */
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boost::shared_ptr<GaussianFactor> linearize(const UrbanConfig& config) const {
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Vector b = zero(6);
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Matrix H1 = zeros(6,6);
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Matrix H2 = zeros(6,6);
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boost::shared_ptr<GaussianFactor> linearized(new GaussianFactor(key1_,
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square_root_inverse_covariance_ * H1, key2_,
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square_root_inverse_covariance_ * H2, b, 1.0));
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return linearized;
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}
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};
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} /// namespace gtsam
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@ -50,7 +50,7 @@ TEST( UrbanGraph, addMeasurement)
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// Check adding a measurement
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UrbanGraph g;
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double sigma = 0.2;// 20 cm
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g.addMeasurement(4, 2, sigma, 1, 1);
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g.addMeasurement(4, 2, sigma, 1, 1); // ground truth would be (5,2)
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LONGS_EQUAL(1,g.size());
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// Create a config
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@ -63,10 +63,30 @@ TEST( UrbanGraph, addMeasurement)
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DOUBLES_EQUAL(expected, g.error(config), 1e-9);
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}
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/* ************************************************************************* */
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TEST( UrbanGraph, addOdometry)
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{
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// Check adding a measurement
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UrbanGraph g;
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double sigmadx = 0.01; // 1 cm
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double sigmayaw = M_PI/180.0;// 1 degree
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g.addOdometry(2, 0, sigmadx,sigmayaw, 1); // 1m forward, 0 yaw
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LONGS_EQUAL(1,g.size());
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// Create a config
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UrbanConfig config;
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config.addRobotPose(1,robotPose);
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config.addLandmark(1,landmark);
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// Check error
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double expected = 0.5/0.01/0.01;
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// TODO DOUBLES_EQUAL(expected, g.error(config), 1e-9);
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}
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/* ************************************************************************* *
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* the measurements are relative to the robot pose so
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* they are in robot coordinate frame different from the global (above)
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*
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*/
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UrbanGraph testGraph() {
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double sigma = 0.2;// 20 cm
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@ -88,7 +108,7 @@ UrbanGraph testGraph() {
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return g;
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}
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/* ************************************************************************* *
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/* ************************************************************************* */
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TEST( UrbanGraph, optimizeLM)
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{
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// build a graph
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@ -103,23 +123,30 @@ TEST( UrbanGraph, optimizeLM)
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initialEstimate->addLandmark(3, landmark3);
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initialEstimate->addLandmark(4, landmark4);
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// Check error vector
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Vector expected = zero(16);
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// TODO CHECK(assert_equal(expected, graph.error_vector(*initialEstimate)));
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// Check error = zero with this config
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//DOUBLES_EQUAL(55, graph.error(*initialEstimate), 1e-9);
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// Create an ordering of the variables
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Ordering ordering;
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ordering += "l1","l2","l3","l4","x1","x2"; // boost assign syntax
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// Create an optimizer and check its error
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// We expect the initial to be zero because config is the ground truth
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//Optimizer optimizer(graph, ordering, initialEstimate, 1e-5);
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//DOUBLES_EQUAL(0, optimizer.error(), 1e-9);
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Optimizer optimizer(graph, ordering, initialEstimate, 1e-5);
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// TODO DOUBLES_EQUAL(0, optimizer.error(), 1e-9);
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// Iterate once, and the config should not have changed because we started
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// with the ground truth
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// Optimizer afterOneIteration = optimizer.iterate();
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// DOUBLES_EQUAL(0, optimizer.error(), 1e-9);
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// TODO DOUBLES_EQUAL(0, optimizer.error(), 1e-9);
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// check if correct
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// CHECK(assert_equal(*initialEstimate,*(afterOneIteration.config())));
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// TODO CHECK(assert_equal(*initialEstimate,*(afterOneIteration.config())));
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}
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/* ************************************************************************* */
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