addOdometry now works

release/4.3a0
Frank Dellaert 2009-12-18 06:55:44 +00:00
parent c987ab397c
commit d11d674c01
5 changed files with 208 additions and 9 deletions

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@ -8,6 +8,8 @@
#include "FactorGraph-inl.h"
#include "NonlinearOptimizer-inl.h"
#include "UrbanGraph.h"
#include "UrbanMeasurement.h"
#include "UrbanOdometry.h"
namespace gtsam {
@ -43,7 +45,12 @@ namespace gtsam {
/* ************************************************************************* */
void UrbanGraph::addOdometry(double dx, double yaw, double sigmadx,
double sigmayaw, int p) {
// TODO
Vector z = Vector_(dx,0,0,yaw,0,0);
Matrix cov = eye(6);
cov(1,1)=sigmadx*sigmadx;
cov(4,4)=sigmadx*sigmadx;
sharedFactor factor(new UrbanOdometry(symbol('x',p),symbol('x',p+1),z,cov));
push_back(factor);
}
/* ************************************************************************* */

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@ -8,7 +8,7 @@
#pragma once
#include "NonlinearFactorGraph.h"
#include "UrbanMeasurement.h"
#include "UrbanFactor.h"
namespace gtsam {

86
cpp/UrbanOdometry.cpp Normal file
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@ -0,0 +1,86 @@
/**
* @file UrbanOdometry.cpp
* @brief A non-linear factor specialized to the Visual SLAM problem
* @author Frank Dellaert
* @author Viorela Ila
*/
#include "UrbanOdometry.h"
using namespace std;
namespace gtsam {
/* ************************************************************************* *
UrbanOdometry::UrbanOdometry() {
/// Arbitrary values
robotPoseNumber_ = 1;
robotPoseName_ = symbol('x',robotPoseNumber_);
keys_.push_back(robotPoseName_);
}
/* ************************************************************************* *
UrbanOdometry::UrbanOdometry(const Point2& z, double sigma, int cn, int ln)
: NonlinearFactor<Pose3Config>(z.vector(), sigma)
{
robotPoseNumber_ = cn;
landmarkNumber_ = ln;
robotPoseName_ = symbol('x',robotPoseNumber_);
landmarkName_ = symbol('l',landmarkNumber_);
keys_.push_back(robotPoseName_);
keys_.push_back(landmarkName_);
}
/* ************************************************************************* *
void UrbanOdometry::print(const std::string& s) const {
printf("%s %s %s\n", s.c_str(), robotPoseName_.c_str(), landmarkName_.c_str());
gtsam::print(this->z_, s+".z");
}
/* ************************************************************************* *
bool UrbanOdometry::equals(const UrbanOdometry& p, double tol) const {
if (&p == NULL) return false;
if (robotPoseNumber_ != p.robotPoseNumber_ || landmarkNumber_ != p.landmarkNumber_) return false;
if (!equal_with_abs_tol(this->z_,p.z_,tol)) return false;
return true;
}
/* ************************************************************************* *
// TODO Implement transformPoint2_from
// the difference here that we have a 2d point in a 3D coordinate
Vector UrbanOdometry::predict(const Pose3Config& c) const {
Pose3 pose = c.cameraPose(robotPoseNumber_);
Point2 landmark = c.landmarkPoint(landmarkNumber_);
return transformPoint2_from(SimpleCamera(*K_,pose), landmark).vector();
}
/* ************************************************************************* *
Vector UrbanOdometry::error_vector(const Pose3Config& c) const {
// Right-hand-side b = (z - h(x))/sigma
Point2 h = predict(c);
return (this->z_ - h);
}
/* ************************************************************************* *
GaussianFactor::shared_ptr UrbanOdometry::linearize(const Pose3Config& c) const
{
// get arguments from config
Pose3 pose = c.cameraPose(robotPoseNumber_);
Point3 landmark = c.landmarkPoint(landmarkNumber_);
SimpleCamera camera(*K_,pose);
// Jacobians
Matrix Dh1 = Dproject_pose(camera, landmark);
Matrix Dh2 = Dproject_point(camera, landmark);
// Right-hand-side b = (z - h(x))
Vector h = project(camera, landmark).vector();
Vector b = this->z_ - h;
// Make new linearfactor, divide by sigma
GaussianFactor::shared_ptr
p(new GaussianFactor(robotPoseName_, Dh1, landmarkName_, Dh2, b, this->sigma_));
return p;
}
/* ************************************************************************* */
} // namespace gtsam

79
cpp/UrbanOdometry.h Normal file
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@ -0,0 +1,79 @@
/**
* @file UrbanOdometry.h
* @brief A Nonlinear Factor, specialized for Urban application
* @author Frank Dellaert
* @author Viorela Ila
*/
#pragma once
/*
#include <map>
#include "NonlinearFactor.h"
#include "GaussianFactor.h"
#include "VectorConfig.h"
#include "Pose3.h"
*/
#include "UrbanFactor.h"
namespace gtsam {
class UrbanOdometry: public UrbanFactor {
private:
std::string key1_, key2_; /** The keys of the two poses, order matters */
Matrix square_root_inverse_covariance_; /** sqrt(inv(measurement_covariance)) */
public:
typedef boost::shared_ptr<UrbanOdometry> shared_ptr; // shorthand for a smart pointer to a factor
UrbanOdometry(const std::string& key1, const std::string& key2,
const Vector& measured, const Matrix& measurement_covariance) :
UrbanFactor(measured, 1.0),
key1_(key1), key2_(key2) {
square_root_inverse_covariance_ = inverse_square_root(
measurement_covariance);
}
/** implement functions needed for Testable */
void print(const std::string& name) const {
std::cout << name << std::endl;
std::cout << "Factor " << std::endl;
std::cout << "key1 " << key1_ << std::endl;
std::cout << "key2 " << key2_ << std::endl;
gtsam::print(z_,"measured ");
gtsam::print(square_root_inverse_covariance_, "MeasurementCovariance");
}
bool equals(const NonlinearFactor<UrbanConfig>& expected, double tol) const {
return false;
}
/** implement functions needed to derive from Factor */
Vector error_vector(const UrbanConfig& config) const {
return zero(6);
}
std::list<std::string> keys() const {
std::list<std::string> l;
return l;
}
std::size_t size() const {
return 2;
}
/** linearize */
boost::shared_ptr<GaussianFactor> linearize(const UrbanConfig& config) const {
Vector b = zero(6);
Matrix H1 = zeros(6,6);
Matrix H2 = zeros(6,6);
boost::shared_ptr<GaussianFactor> linearized(new GaussianFactor(key1_,
square_root_inverse_covariance_ * H1, key2_,
square_root_inverse_covariance_ * H2, b, 1.0));
return linearized;
}
};
} /// namespace gtsam

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@ -50,7 +50,7 @@ TEST( UrbanGraph, addMeasurement)
// Check adding a measurement
UrbanGraph g;
double sigma = 0.2;// 20 cm
g.addMeasurement(4, 2, sigma, 1, 1);
g.addMeasurement(4, 2, sigma, 1, 1); // ground truth would be (5,2)
LONGS_EQUAL(1,g.size());
// Create a config
@ -63,10 +63,30 @@ TEST( UrbanGraph, addMeasurement)
DOUBLES_EQUAL(expected, g.error(config), 1e-9);
}
/* ************************************************************************* */
TEST( UrbanGraph, addOdometry)
{
// Check adding a measurement
UrbanGraph g;
double sigmadx = 0.01; // 1 cm
double sigmayaw = M_PI/180.0;// 1 degree
g.addOdometry(2, 0, sigmadx,sigmayaw, 1); // 1m forward, 0 yaw
LONGS_EQUAL(1,g.size());
// Create a config
UrbanConfig config;
config.addRobotPose(1,robotPose);
config.addLandmark(1,landmark);
// Check error
double expected = 0.5/0.01/0.01;
// TODO DOUBLES_EQUAL(expected, g.error(config), 1e-9);
}
/* ************************************************************************* *
* the measurements are relative to the robot pose so
* they are in robot coordinate frame different from the global (above)
*
*/
UrbanGraph testGraph() {
double sigma = 0.2;// 20 cm
@ -88,7 +108,7 @@ UrbanGraph testGraph() {
return g;
}
/* ************************************************************************* *
/* ************************************************************************* */
TEST( UrbanGraph, optimizeLM)
{
// build a graph
@ -103,23 +123,30 @@ TEST( UrbanGraph, optimizeLM)
initialEstimate->addLandmark(3, landmark3);
initialEstimate->addLandmark(4, landmark4);
// Check error vector
Vector expected = zero(16);
// TODO CHECK(assert_equal(expected, graph.error_vector(*initialEstimate)));
// Check error = zero with this config
//DOUBLES_EQUAL(55, graph.error(*initialEstimate), 1e-9);
// Create an ordering of the variables
Ordering ordering;
ordering += "l1","l2","l3","l4","x1","x2"; // boost assign syntax
// Create an optimizer and check its error
// We expect the initial to be zero because config is the ground truth
//Optimizer optimizer(graph, ordering, initialEstimate, 1e-5);
//DOUBLES_EQUAL(0, optimizer.error(), 1e-9);
Optimizer optimizer(graph, ordering, initialEstimate, 1e-5);
// TODO DOUBLES_EQUAL(0, optimizer.error(), 1e-9);
// Iterate once, and the config should not have changed because we started
// with the ground truth
// Optimizer afterOneIteration = optimizer.iterate();
// DOUBLES_EQUAL(0, optimizer.error(), 1e-9);
// TODO DOUBLES_EQUAL(0, optimizer.error(), 1e-9);
// check if correct
// CHECK(assert_equal(*initialEstimate,*(afterOneIteration.config())));
// TODO CHECK(assert_equal(*initialEstimate,*(afterOneIteration.config())));
}
/* ************************************************************************* */