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Merge branch 'develop' into improved-hybrid-api

release/4.3a0
Varun Agrawal 2024-09-05 10:04:49 -04:00
parent 8214de04d1 232fa02b19
commit c7e1c60224
17 changed files with 212 additions and 239 deletions
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14
CMakeLists.txt
View File

@ -1,4 +1,16 @@
cmake_minimum_required(VERSION 3.0)
cmake_minimum_required(VERSION 3.5)
if (POLICY CMP0082)
cmake_policy(SET CMP0082 NEW) # install from sub-directories immediately
endif()
if (POLICY CMP0102)
cmake_policy(SET CMP0102 NEW) # set policy on advanced variables and cmake cache
endif()
if (POLICY CMP0156)
cmake_policy(SET CMP0156 NEW) # new linker strategies
endif()
if (POLICY CMP0167)
cmake_policy(SET CMP0167 OLD) # Don't complain about boost
endif()
# Set the version number for the library
set (GTSAM_VERSION_MAJOR 4)

1
CppUnitLite/CMakeLists.txt
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@ -6,6 +6,7 @@ file(GLOB cppunitlite_src "*.cpp")
add_library(CppUnitLite STATIC ${cppunitlite_src} ${cppunitlite_headers})
list(APPEND GTSAM_EXPORTED_TARGETS CppUnitLite)
set(GTSAM_EXPORTED_TARGETS "${GTSAM_EXPORTED_TARGETS}" PARENT_SCOPE)
target_compile_features(CppUnitLite PUBLIC ${GTSAM_COMPILE_FEATURES_PUBLIC})
gtsam_assign_source_folders("${cppunitlite_headers};${cppunitlite_src}") # MSVC project structure

2
cmake/HandleBoost.cmake
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@ -25,7 +25,7 @@ endif()
set(BOOST_FIND_MINIMUM_VERSION 1.65)
set(BOOST_FIND_MINIMUM_COMPONENTS serialization system filesystem thread program_options date_time timer chrono regex)
find_package(Boost ${BOOST_FIND_MINIMUM_VERSION} COMPONENTS ${BOOST_FIND_MINIMUM_COMPONENTS})
find_package(Boost ${BOOST_FIND_MINIMUM_VERSION} COMPONENTS ${BOOST_FIND_MINIMUM_COMPONENTS} REQUIRED)
# Required components
if(NOT Boost_SERIALIZATION_LIBRARY OR NOT Boost_SYSTEM_LIBRARY OR NOT Boost_FILESYSTEM_LIBRARY OR

2
cmake/example_cmake_find_gtsam/CMakeLists.txt
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@ -1,7 +1,7 @@
# This file shows how to build and link a user project against GTSAM using CMake
###################################################################################
# To create your own project, replace "example" with the actual name of your project
cmake_minimum_required(VERSION 3.0)
cmake_minimum_required(VERSION 3.5)
project(example CXX)
# Find GTSAM, either from a local build, or from a Debian/Ubuntu package.

41
gtsam/hybrid/GaussianMixture.cpp
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@ -330,10 +330,10 @@ AlgebraicDecisionTree<Key> GaussianMixture::logProbability(
return DecisionTree<Key, double>(conditionals_, probFunc);
}
/* *******************************************************************************/
AlgebraicDecisionTree<Key> GaussianMixture::errorTree(
/* ************************************************************************* */
double GaussianMixture::conditionalError(
const GaussianConditional::shared_ptr &conditional,
const VectorValues &continuousValues) const {
auto errorFunc = [&](const GaussianConditional::shared_ptr &conditional) {
// Check if valid pointer
if (conditional) {
return conditional->error(continuousValues) + //
@ -341,8 +341,17 @@ AlgebraicDecisionTree<Key> GaussianMixture::errorTree(
} else {
// If not valid, pointer, it means this conditional was pruned,
// so we return maximum error.
// This way the negative exponential will give
// a probability value close to 0.0.
return std::numeric_limits<double>::max();
}
}
/* *******************************************************************************/
AlgebraicDecisionTree<Key> GaussianMixture::errorTree(
const VectorValues &continuousValues) const {
auto errorFunc = [&](const GaussianConditional::shared_ptr &conditional) {
return conditionalError(conditional, continuousValues);
};
DecisionTree<Key, double> error_tree(conditionals_, errorFunc);
return error_tree;
@ -350,33 +359,9 @@ AlgebraicDecisionTree<Key> GaussianMixture::errorTree(
/* *******************************************************************************/
double GaussianMixture::error(const HybridValues &values) const {
// Check if discrete keys in discrete assignment are
// present in the GaussianMixture
KeyVector dKeys = this->discreteKeys_.indices();
bool valid_assignment = false;
for (auto &&kv : values.discrete()) {
if (std::find(dKeys.begin(), dKeys.end(), kv.first) != dKeys.end()) {
valid_assignment = true;
break;
}
}
// The discrete assignment is not valid so we throw an error.
if (!valid_assignment) {
throw std::runtime_error(
"Invalid discrete values in values. Not all discrete keys specified.");
}
// Directly index to get the conditional, no need to build the whole tree.
auto conditional = conditionals_(values.discrete());
if (conditional) {
return conditional->error(values.continuous()) + //
logConstant_ - conditional->logNormalizationConstant();
} else {
// If not valid, pointer, it means this conditional was pruned,
// so we return maximum error.
return std::numeric_limits<double>::max();
}
return conditionalError(conditional, values.continuous());
}
/* *******************************************************************************/

4
gtsam/hybrid/GaussianMixture.h
View File

@ -256,6 +256,10 @@ class GTSAM_EXPORT GaussianMixture
/// Check whether `given` has values for all frontal keys.
bool allFrontalsGiven(const VectorValues &given) const;
/// Helper method to compute the error of a conditional.
double conditionalError(const GaussianConditional::shared_ptr &conditional,
const VectorValues &continuousValues) const;
#ifdef GTSAM_ENABLE_BOOST_SERIALIZATION
/** Serialization function */
friend class boost::serialization::access;

9
gtsam/hybrid/HybridFactorGraph.cpp
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@ -49,15 +49,6 @@ KeySet HybridFactorGraph::discreteKeySet() const {
return keys;
}
/* ************************************************************************* */
std::unordered_map<Key, DiscreteKey> HybridFactorGraph::discreteKeyMap() const {
std::unordered_map<Key, DiscreteKey> result;
for (const DiscreteKey& k : discreteKeys()) {
result[k.first] = k;
}
return result;
}
/* ************************************************************************* */
const KeySet HybridFactorGraph::continuousKeySet() const {
KeySet keys;

3
gtsam/hybrid/HybridFactorGraph.h
View File

@ -69,9 +69,6 @@ class GTSAM_EXPORT HybridFactorGraph : public FactorGraph<Factor> {
/// Get all the discrete keys in the factor graph, as a set of Keys.
KeySet discreteKeySet() const;
/// Get a map from Key to corresponding DiscreteKey.
std::unordered_map<Key, DiscreteKey> discreteKeyMap() const;
/// Get all the continuous keys in the factor graph.
const KeySet continuousKeySet() const;

20
gtsam/hybrid/HybridGaussianFactorGraph.cpp
View File

@ -312,8 +312,8 @@ using Result = std::pair<std::shared_ptr<GaussianConditional>,
GaussianMixtureFactor::sharedFactor>;
/**
* Compute the probability q(μ;m) = exp(-error(μ;m)) * sqrt(det(2π Σ_m)
* from the residual error at the mean μ.
* Compute the probability p(μ;m) = exp(-error(μ;m)) * sqrt(det(2π Σ_m)
* from the residual error ||b||^2 at the mean μ.
* The residual error contains no keys, and only
* depends on the discrete separator if present.
*/
@ -523,19 +523,9 @@ EliminateHybrid(const HybridGaussianFactorGraph &factors,
std::inserter(continuousSeparator, continuousSeparator.begin()));
// Similarly for the discrete separator.
auto discreteKeySet = factors.discreteKeySet();
// Use set-difference to get the difference in keys
KeySet discreteSeparatorSet;
std::set_difference(
discreteKeySet.begin(), discreteKeySet.end(), frontalKeysSet.begin(),
frontalKeysSet.end(),
std::inserter(discreteSeparatorSet, discreteSeparatorSet.begin()));
// Convert from set of keys to set of DiscreteKeys
std::set<DiscreteKey> discreteSeparator;
auto discreteKeyMap = factors.discreteKeyMap();
for (auto key : discreteSeparatorSet) {
discreteSeparator.insert(discreteKeyMap.at(key));
}
// Since we eliminate all continuous variables first,
// the discrete separator will be *all* the discrete keys.
std::set<DiscreteKey> discreteSeparator = factors.discreteKeys();
return hybridElimination(factors, frontalKeys, continuousSeparator,
discreteSeparator);

8
gtsam/hybrid/tests/testGaussianMixtureFactor.cpp
View File

@ -434,12 +434,12 @@ static HybridBayesNet CreateBayesNet(double mu0, double mu1, double sigma0,
/* ************************************************************************* */
/**
* Test a model P(x0)P(z0|x0)P(x1|x0,m1)P(z1|x1)P(m1).
* Test a model P(z0|x0)P(x1|x0,m1)P(z1|x1)P(m1).
*
* P(f01|x1,x0,m1) has different means and same covariance.
*
* Converting to a factor graph gives us
* P(x0)ϕ(x0)ϕ(x1,x0,m1)ϕ(x1)P(m1)
* ϕ(x0)ϕ(x1,x0,m1)ϕ(x1)P(m1)
*
* If we only have a measurement on z0, then
* the probability of m1 should be 0.5/0.5.
@ -488,12 +488,12 @@ TEST(GaussianMixtureFactor, TwoStateModel) {
/* ************************************************************************* */
/**
* Test a model P(x0)P(z0|x0)P(x1|x0,m1)P(z1|x1)P(m1).
* Test a model P(z0|x0)P(x1|x0,m1)P(z1|x1)P(m1).
*
* P(f01|x1,x0,m1) has different means and different covariances.
*
* Converting to a factor graph gives us
* P(x0)ϕ(x0)ϕ(x1,x0,m1)ϕ(x1)P(m1)
* ϕ(x0)ϕ(x1,x0,m1)ϕ(x1)P(m1)
*
* If we only have a measurement on z0, then
* the P(m1) should be 0.5/0.5.

150
gtsam/hybrid/tests/testMixtureFactor.cpp
View File

@ -118,156 +118,6 @@ TEST(MixtureFactor, Dim) {
EXPECT_LONGS_EQUAL(1, mixtureFactor.dim());
}
/* ************************************************************************* */
// Test components with differing means
TEST(MixtureFactor, DifferentMeans) {
DiscreteKey m1(M(1), 2), m2(M(2), 2);
Values values;
double x1 = 0.0, x2 = 1.75, x3 = 2.60;
values.insert(X(1), x1);
values.insert(X(2), x2);
values.insert(X(3), x3);
auto model0 = noiseModel::Isotropic::Sigma(1, 1e-0);
auto model1 = noiseModel::Isotropic::Sigma(1, 1e-0);
auto prior_noise = noiseModel::Isotropic::Sigma(1, 1e-0);
auto f0 = std::make_shared<BetweenFactor<double>>(X(1), X(2), 0.0, model0);
auto f1 = std::make_shared<BetweenFactor<double>>(X(1), X(2), 2.0, model1);
std::vector<NonlinearFactor::shared_ptr> factors{f0, f1};
MixtureFactor mixtureFactor({X(1), X(2)}, {m1}, factors);
HybridNonlinearFactorGraph hnfg;
hnfg.push_back(mixtureFactor);
f0 = std::make_shared<BetweenFactor<double>>(X(2), X(3), 0.0, model0);
f1 = std::make_shared<BetweenFactor<double>>(X(2), X(3), 2.0, model1);
std::vector<NonlinearFactor::shared_ptr> factors23{f0, f1};
hnfg.push_back(MixtureFactor({X(2), X(3)}, {m2}, factors23));
auto prior = PriorFactor<double>(X(1), x1, prior_noise);
hnfg.push_back(prior);
hnfg.emplace_shared<PriorFactor<double>>(X(2), 2.0, prior_noise);
auto hgfg = hnfg.linearize(values);
auto bn = hgfg->eliminateSequential();
HybridValues actual = bn->optimize();
HybridValues expected(
VectorValues{
{X(1), Vector1(0.0)}, {X(2), Vector1(0.25)}, {X(3), Vector1(-0.6)}},
DiscreteValues{{M(1), 1}, {M(2), 0}});
EXPECT(assert_equal(expected, actual));
{
DiscreteValues dv{{M(1), 0}, {M(2), 0}};
VectorValues cont = bn->optimize(dv);
double error = bn->error(HybridValues(cont, dv));
// regression
EXPECT_DOUBLES_EQUAL(1.77418393408, error, 1e-9);
}
{
DiscreteValues dv{{M(1), 0}, {M(2), 1}};
VectorValues cont = bn->optimize(dv);
double error = bn->error(HybridValues(cont, dv));
// regression
EXPECT_DOUBLES_EQUAL(1.77418393408, error, 1e-9);
}
{
DiscreteValues dv{{M(1), 1}, {M(2), 0}};
VectorValues cont = bn->optimize(dv);
double error = bn->error(HybridValues(cont, dv));
// regression
EXPECT_DOUBLES_EQUAL(1.10751726741, error, 1e-9);
}
{
DiscreteValues dv{{M(1), 1}, {M(2), 1}};
VectorValues cont = bn->optimize(dv);
double error = bn->error(HybridValues(cont, dv));
// regression
EXPECT_DOUBLES_EQUAL(1.10751726741, error, 1e-9);
}
}
/* ************************************************************************* */
// Test components with differing covariances
TEST(MixtureFactor, DifferentCovariances) {
DiscreteKey m1(M(1), 2);
Values values;
double x1 = 1.0, x2 = 1.0;
values.insert(X(1), x1);
values.insert(X(2), x2);
double between = 0.0;
auto model0 = noiseModel::Isotropic::Sigma(1, 1e2);
auto model1 = noiseModel::Isotropic::Sigma(1, 1e-2);
auto prior_noise = noiseModel::Isotropic::Sigma(1, 1e-3);
auto f0 =
std::make_shared<BetweenFactor<double>>(X(1), X(2), between, model0);
auto f1 =
std::make_shared<BetweenFactor<double>>(X(1), X(2), between, model1);
std::vector<NonlinearFactor::shared_ptr> factors{f0, f1};
// Create via toFactorGraph
using symbol_shorthand::Z;
Matrix H0_1, H0_2, H1_1, H1_2;
Vector d0 = f0->evaluateError(x1, x2, &H0_1, &H0_2);
std::vector<std::pair<Key, Matrix>> terms0 = {{Z(1), gtsam::I_1x1 /*Rx*/},
//
{X(1), H0_1 /*Sp1*/},
{X(2), H0_2 /*Tp2*/}};
Vector d1 = f1->evaluateError(x1, x2, &H1_1, &H1_2);
std::vector<std::pair<Key, Matrix>> terms1 = {{Z(1), gtsam::I_1x1 /*Rx*/},
//
{X(1), H1_1 /*Sp1*/},
{X(2), H1_2 /*Tp2*/}};
auto gm = new gtsam::GaussianMixture(
{Z(1)}, {X(1), X(2)}, {m1},
{std::make_shared<GaussianConditional>(terms0, 1, -d0, model0),
std::make_shared<GaussianConditional>(terms1, 1, -d1, model1)});
gtsam::HybridBayesNet bn;
bn.emplace_back(gm);
gtsam::VectorValues measurements;
measurements.insert(Z(1), gtsam::Z_1x1);
// Create FG with single GaussianMixtureFactor
HybridGaussianFactorGraph mixture_fg = bn.toFactorGraph(measurements);
// Linearized prior factor on X1
auto prior = PriorFactor<double>(X(1), x1, prior_noise).linearize(values);
mixture_fg.push_back(prior);
auto hbn = mixture_fg.eliminateSequential();
VectorValues cv;
cv.insert(X(1), Vector1(0.0));
cv.insert(X(2), Vector1(0.0));
// P(m1) = [0.5, 0.5], so we should pick 0
DiscreteValues dv;
dv.insert({M(1), 0});
HybridValues expected_values(cv, dv);
HybridValues actual_values = hbn->optimize();
EXPECT(assert_equal(expected_values, actual_values));
// Check that we get different error values at the MLE point μ.
AlgebraicDecisionTree<Key> errorTree = hbn->errorTree(cv);
HybridValues hv0(cv, DiscreteValues{{M(1), 0}});
HybridValues hv1(cv, DiscreteValues{{M(1), 1}});
AlgebraicDecisionTree<Key> expectedErrorTree(m1, 9.90348755254,
0.69314718056);
EXPECT(assert_equal(expectedErrorTree, errorTree));
}
/* ************************************************************************* */
int main() {
TestResult tr;

6
gtsam/linear/IterativeSolver.cpp
View File

@ -46,6 +46,12 @@ void IterativeOptimizationParameters::print(ostream &os) const {
<< verbosityTranslator(verbosity_) << endl;
}
/*****************************************************************************/
bool IterativeOptimizationParameters::equals(
const IterativeOptimizationParameters &other, double tol) const {
return verbosity_ == other.verbosity();
}
/*****************************************************************************/
ostream& operator<<(ostream &os, const IterativeOptimizationParameters &p) {
p.print(os);

16
gtsam/linear/IterativeSolver.h
View File

@ -41,15 +41,14 @@ class VectorValues;
* parameters for iterative linear solvers
*/
class IterativeOptimizationParameters {
public:
public:
typedef std::shared_ptr<IterativeOptimizationParameters> shared_ptr;
enum Verbosity {
SILENT = 0, COMPLEXITY, ERROR
} verbosity_;
enum Verbosity { SILENT = 0, COMPLEXITY, ERROR };
public:
protected:
Verbosity verbosity_;
public:
IterativeOptimizationParameters(Verbosity v = SILENT) :
verbosity_(v) {
@ -71,6 +70,9 @@ public:
/* virtual print function */
GTSAM_EXPORT virtual void print(std::ostream &os) const;
GTSAM_EXPORT virtual bool equals(const IterativeOptimizationParameters &other,
double tol = 1e-9) const;
/* for serialization */
GTSAM_EXPORT friend std::ostream &operator<<(
std::ostream &os, const IterativeOptimizationParameters &p);

6
gtsam/nonlinear/LevenbergMarquardtOptimizer.cpp
View File

@ -236,9 +236,9 @@ bool LevenbergMarquardtOptimizer::tryLambda(const GaussianFactorGraph& linear,
if (currentState->iterations == 0) {
cout << "iter cost cost_change lambda success iter_time" << endl;
}
cout << setw(4) << currentState->iterations << " " << setw(8) << newError << " " << setw(3) << setprecision(2)
<< costChange << " " << setw(3) << setprecision(2) << currentState->lambda << " " << setw(4)
<< systemSolvedSuccessfully << " " << setw(3) << setprecision(2) << iterationTime << endl;
cout << setw(4) << currentState->iterations << " " << setw(12) << newError << " " << setw(12) << setprecision(2)
<< costChange << " " << setw(10) << setprecision(2) << currentState->lambda << " " << setw(6)
<< systemSolvedSuccessfully << " " << setw(10) << setprecision(2) << iterationTime << endl;
}
if (step_is_successful) {
// we have successfully decreased the cost and we have good modelFidelity

22
gtsam/nonlinear/NonlinearOptimizerParams.cpp
View File

@ -123,6 +123,28 @@ void NonlinearOptimizerParams::print(const std::string& str) const {
std::cout.flush();
}
/* ************************************************************************* */
bool NonlinearOptimizerParams::equals(const NonlinearOptimizerParams& other,
double tol) const {
// Check for equality of shared ptrs
bool iterative_params_equal = iterativeParams == other.iterativeParams;
// Check equality of components
if (iterativeParams && other.iterativeParams) {
iterative_params_equal = iterativeParams->equals(*other.iterativeParams);
} else {
// Check if either is null. If both are null, then true
iterative_params_equal = !iterativeParams && !other.iterativeParams;
}
return maxIterations == other.getMaxIterations() &&
std::abs(relativeErrorTol - other.getRelativeErrorTol()) <= tol &&
std::abs(absoluteErrorTol - other.getAbsoluteErrorTol()) <= tol &&
std::abs(errorTol - other.getErrorTol()) <= tol &&
verbosityTranslator(verbosity) == other.getVerbosity() &&
orderingType == other.orderingType && ordering == other.ordering &&
linearSolverType == other.linearSolverType && iterative_params_equal;
}
/* ************************************************************************* */
std::string NonlinearOptimizerParams::linearSolverTranslator(
LinearSolverType linearSolverType) const {

11
gtsam/nonlinear/NonlinearOptimizerParams.h
View File

@ -114,16 +114,7 @@ public:
virtual void print(const std::string& str = "") const;
bool equals(const NonlinearOptimizerParams& other, double tol = 1e-9) const {
return maxIterations == other.getMaxIterations()
&& std::abs(relativeErrorTol - other.getRelativeErrorTol()) <= tol
&& std::abs(absoluteErrorTol - other.getAbsoluteErrorTol()) <= tol
&& std::abs(errorTol - other.getErrorTol()) <= tol
&& verbosityTranslator(verbosity) == other.getVerbosity();
// && orderingType.equals(other.getOrderingType()_;
// && linearSolverType == other.getLinearSolverType();
// TODO: check ordering, iterativeParams, and iterationsHook
}
bool equals(const NonlinearOptimizerParams& other, double tol = 1e-9) const;
inline bool isMultifrontal() const {
return (linearSolverType == MULTIFRONTAL_CHOLESKY)

122
python/gtsam/examples/SelfCalibrationExample.py Normal file
View File

@ -0,0 +1,122 @@
# pylint: disable=unused-import,consider-using-from-import,invalid-name,no-name-in-module,no-member,missing-function-docstring,too-many-locals
"""
Transcription of SelfCalibrationExample.cpp
"""
import math
from gtsam import Cal3_S2
from gtsam.noiseModel import Diagonal, Isotropic
# SFM-specific factors
from gtsam import GeneralSFMFactor2Cal3_S2 # does calibration !
from gtsam import PinholeCameraCal3_S2
# Camera observations of landmarks (i.e. pixel coordinates) will be stored as Point2 (x, y).
from gtsam import Point2
from gtsam import Point3, Pose3, Rot3
# Inference and optimization
from gtsam import NonlinearFactorGraph, DoglegOptimizer, Values
from gtsam.symbol_shorthand import K, L, X
# this is a direct translation of examples/SFMData.h
# which is slightly different from python/gtsam/examples/SFMdata.py.
def createPoints() -> list[Point3]:
"""
Create the set of ground-truth landmarks
"""
return [
Point3(10.0, 10.0, 10.0),
Point3(-10.0, 10.0, 10.0),
Point3(-10.0, -10.0, 10.0),
Point3(10.0, -10.0, 10.0),
Point3(10.0, 10.0, -10.0),
Point3(-10.0, 10.0, -10.0),
Point3(-10.0, -10.0, -10.0),
Point3(10.0, -10.0, -10.0),
]
def createPoses(
init: Pose3 = Pose3(Rot3.Ypr(math.pi / 2, 0, -math.pi / 2), Point3(30, 0, 0)),
delta: Pose3 = Pose3(
Rot3.Ypr(0, -math.pi / 4, 0),
Point3(math.sin(math.pi / 4) * 30, 0, 30 * (1 - math.sin(math.pi / 4))),
),
steps: int = 8,
) -> list[Pose3]:
"""
Create the set of ground-truth poses
Default values give a circular trajectory,
radius 30 at pi/4 intervals, always facing the circle center
"""
poses: list[Pose3] = []
poses.append(init)
for i in range(1, steps):
poses.append(poses[i - 1].compose(delta))
return poses
def main() -> None:
# Create the set of ground-truth
points: list[Point3] = createPoints()
poses: list[Pose3] = createPoses()
# Create the factor graph
graph = NonlinearFactorGraph()
# Add a prior on pose x1.
# 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
poseNoise = Diagonal.Sigmas([0.1, 0.1, 0.1, 0.3, 0.3, 0.3])
graph.addPriorPose3(X(0), poses[0], poseNoise)
# Simulated measurements from each camera pose, adding them to the factor graph
Kcal = Cal3_S2(50.0, 50.0, 0.0, 50.0, 50.0)
measurementNoise = Isotropic.Sigma(2, 1.0)
for i, pose in enumerate(poses):
for j, point in enumerate(points):
camera = PinholeCameraCal3_S2(pose, Kcal)
measurement: Point2 = camera.project(point)
# The only real difference with the Visual SLAM example is that here we
# use a different factor type, that also calculates the Jacobian with
# respect to calibration
graph.add(
GeneralSFMFactor2Cal3_S2(
measurement,
measurementNoise,
X(i),
L(j),
K(0),
)
)
# Add a prior on the position of the first landmark.
pointNoise = Isotropic.Sigma(3, 0.1)
graph.addPriorPoint3(L(0), points[0], pointNoise) # add directly to graph
# Add a prior on the calibration.
calNoise = Diagonal.Sigmas([500, 500, 0.1, 100, 100])
graph.addPriorCal3_S2(K(0), Kcal, calNoise)
# Create the initial estimate to the solution
# now including an estimate on the camera calibration parameters
initialEstimate = Values()
initialEstimate.insert(K(0), Cal3_S2(60.0, 60.0, 0.0, 45.0, 45.0))
for i, pose in enumerate(poses):
initialEstimate.insert(
X(i),
pose.compose(
Pose3(Rot3.Rodrigues(-0.1, 0.2, 0.25), Point3(0.05, -0.10, 0.20))
),
)
for j, point in enumerate(points):
initialEstimate.insert(L(j), point + Point3(-0.25, 0.20, 0.15))
# Optimize the graph and print results
result: Values = DoglegOptimizer(graph, initialEstimate).optimize()
result.print("Final results:\n")
if __name__ == "__main__":
main()