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Merge branch 'develop' into improved-api-3

release/4.3a0
Varun Agrawal 2024-09-22 19:36:25 -04:00
parent d3971b93c8 e52973b72d
commit 796d85d7fa
7 changed files with 244 additions and 115 deletions
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2
gtsam/hybrid/HybridGaussianFactor.cpp
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@ -38,7 +38,7 @@ namespace gtsam {
* Gaussian factor in factors.
* @return HybridGaussianFactor::Factors
*/
HybridGaussianFactor::Factors augment(
static HybridGaussianFactor::Factors augment(
const HybridGaussianFactor::FactorValuePairs &factors) {
// Find the minimum value so we can "proselytize" to positive values.
// Done because we can't have sqrt of negative numbers.

51
gtsam/hybrid/HybridGaussianFactor.h
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@ -89,25 +89,28 @@ class GTSAM_EXPORT HybridGaussianFactor : public HybridFactor {
HybridGaussianFactor() = default;
/**
* @brief Construct a new hybrid Gaussian factor.
*
* @param continuousKeys A vector of keys representing continuous variables.
* @param discreteKeys A vector of keys representing discrete variables and
* their cardinalities.
* @param factors The decision tree of Gaussian factors and arbitrary scalars.
*/
HybridGaussianFactor(const KeyVector &continuousKeys,
const DiscreteKeys &discreteKeys,
const FactorValuePairs &factors);
/**
* @brief Construct a new HybridGaussianFactor object using a vector of
* GaussianFactor shared pointers.
* @brief Construct a new HybridGaussianFactor on a single discrete key,
* providing the factors for each mode m as a vector of factors ϕ_m(x).
* The value ϕ(x,m) for the factor is simply ϕ_m(x).
*
* @param continuousKeys Vector of keys for continuous factors.
* @param discreteKey The discrete key to index each component.
* @param factors Vector of gaussian factor shared pointers
* and arbitrary scalars. Same size as the cardinality of discreteKey.
* @param discreteKey The discrete key for the "mode", indexing components.
* @param factors Vector of gaussian factors, one for each mode.
*/
HybridGaussianFactor(const KeyVector &continuousKeys,
const DiscreteKey &discreteKey,
const std::vector<GaussianFactor::shared_ptr> &factors)
: Base(continuousKeys, {discreteKey}), factors_({discreteKey}, factors) {}
/**
* @brief Construct a new HybridGaussianFactor on a single discrete key,
* including a scalar error value for each mode m. The factors and scalars are
* provided as a vector of pairs (ϕ_m(x), E_m).
* The value ϕ(x,m) for the factor is now ϕ_m(x) + E_m.
*
* @param continuousKeys Vector of keys for continuous factors.
* @param discreteKey The discrete key for the "mode", indexing components.
* @param factors Vector of gaussian factor-scalar pairs, one per mode.
*/
HybridGaussianFactor(const KeyVector &continuousKeys,
const DiscreteKey &discreteKey,
@ -115,6 +118,20 @@ class GTSAM_EXPORT HybridGaussianFactor : public HybridFactor {
: HybridGaussianFactor(continuousKeys, {discreteKey},
FactorValuePairs({discreteKey}, factors)) {}
/**
* @brief Construct a new HybridGaussianFactor on a several discrete keys M,
* including a scalar error value for each assignment m. The factors and
* scalars are provided as a DecisionTree<Key> of pairs (ϕ_M(x), E_M).
* The value ϕ(x,M) for the factor is again ϕ_m(x) + E_m.
*
* @param continuousKeys A vector of keys representing continuous variables.
* @param discreteKeys Discrete variables and their cardinalities.
* @param factors The decision tree of Gaussian factor/scalar pairs.
*/
HybridGaussianFactor(const KeyVector &continuousKeys,
const DiscreteKeys &discreteKeys,
const FactorValuePairs &factors);
/// @}
/// @name Testable
/// @{

34
gtsam/hybrid/tests/Switching.h
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@ -31,6 +31,9 @@
#include <vector>
#include "gtsam/linear/GaussianFactor.h"
#include "gtsam/linear/GaussianFactorGraph.h"
#pragma once
namespace gtsam {
@ -44,33 +47,28 @@ using symbol_shorthand::X;
* system which depends on a discrete mode at each time step of the chain.
*
* @param n The number of chain elements.
* @param keyFunc The functional to help specify the continuous key.
* @param dKeyFunc The functional to help specify the discrete key.
* @param x The functional to help specify the continuous key.
* @param m The functional to help specify the discrete key.
* @return HybridGaussianFactorGraph::shared_ptr
*/
inline HybridGaussianFactorGraph::shared_ptr makeSwitchingChain(
size_t n, std::function<Key(int)> keyFunc = X,
std::function<Key(int)> dKeyFunc = M) {
size_t n, std::function<Key(int)> x = X, std::function<Key(int)> m = M) {
HybridGaussianFactorGraph hfg;
hfg.add(JacobianFactor(keyFunc(1), I_3x3, Z_3x1));
hfg.add(JacobianFactor(x(1), I_3x3, Z_3x1));
// keyFunc(1) to keyFunc(n+1)
// x(1) to x(n+1)
for (size_t t = 1; t < n; t++) {
DiscreteKeys dKeys{{dKeyFunc(t), 2}};
HybridGaussianFactor::FactorValuePairs components(
dKeys, {{std::make_shared<JacobianFactor>(keyFunc(t), I_3x3,
keyFunc(t + 1), I_3x3, Z_3x1),
0.0},
{std::make_shared<JacobianFactor>(
keyFunc(t), I_3x3, keyFunc(t + 1), I_3x3, Vector3::Ones()),
0.0}});
hfg.add(
HybridGaussianFactor({keyFunc(t), keyFunc(t + 1)}, dKeys, components));
DiscreteKeys dKeys{{m(t), 2}};
std::vector<GaussianFactor::shared_ptr> components;
components.emplace_back(
new JacobianFactor(x(t), I_3x3, x(t + 1), I_3x3, Z_3x1));
components.emplace_back(
new JacobianFactor(x(t), I_3x3, x(t + 1), I_3x3, Vector3::Ones()));
hfg.add(HybridGaussianFactor({x(t), x(t + 1)}, {m(t), 2}, components));
if (t > 1) {
hfg.add(DecisionTreeFactor({{dKeyFunc(t - 1), 2}, {dKeyFunc(t), 2}},
"0 1 1 3"));
hfg.add(DecisionTreeFactor({{m(t - 1), 2}, {m(t), 2}}, "0 1 1 3"));
}
}

8
gtsam/hybrid/tests/testHybridFactorGraph.cpp
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@ -52,10 +52,10 @@ TEST(HybridFactorGraph, Keys) {
// Add a hybrid Gaussian factor ϕ(x1, c1)
DiscreteKey m1(M(1), 2);
DecisionTree<Key, GaussianFactorValuePair> dt(
M(1), {std::make_shared<JacobianFactor>(X(1), I_3x3, Z_3x1), 0.0},
{std::make_shared<JacobianFactor>(X(1), I_3x3, Vector3::Ones()), 0.0});
hfg.add(HybridGaussianFactor({X(1)}, {m1}, dt));
std::vector<GaussianFactor::shared_ptr> components{
std::make_shared<JacobianFactor>(X(1), I_3x3, Z_3x1),
std::make_shared<JacobianFactor>(X(1), I_3x3, Vector3::Ones())};
hfg.add(HybridGaussianFactor({X(1)}, m1, components));
KeySet expected_continuous{X(0), X(1)};
EXPECT(

54
gtsam/hybrid/tests/testHybridGaussianFactor.cpp
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@ -55,31 +55,48 @@ TEST(HybridGaussianFactor, Constructor) {
}
/* ************************************************************************* */
// "Add" two hybrid factors together.
TEST(HybridGaussianFactor, Sum) {
DiscreteKey m1(1, 2), m2(2, 3);
namespace test_constructor {
DiscreteKey m1(1, 2);
auto A1 = Matrix::Zero(2, 1);
auto A2 = Matrix::Zero(2, 2);
auto A3 = Matrix::Zero(2, 3);
auto b = Matrix::Zero(2, 1);
Vector2 sigmas;
sigmas << 1, 2;
auto f10 = std::make_shared<JacobianFactor>(X(1), A1, X(2), A2, b);
auto f11 = std::make_shared<JacobianFactor>(X(1), A1, X(2), A2, b);
} // namespace test_constructor
/* ************************************************************************* */
// Test simple to complex constructors...
TEST(HybridGaussianFactor, ConstructorVariants) {
using namespace test_constructor;
HybridGaussianFactor fromFactors({X(1), X(2)}, m1, {f10, f11});
std::vector<GaussianFactorValuePair> pairs{{f10, 0.0}, {f11, 0.0}};
HybridGaussianFactor fromPairs({X(1), X(2)}, {m1}, pairs);
assert_equal(fromFactors, fromPairs);
HybridGaussianFactor::FactorValuePairs decisionTree({m1}, pairs);
HybridGaussianFactor fromDecisionTree({X(1), X(2)}, {m1}, decisionTree);
assert_equal(fromDecisionTree, fromPairs);
}
/* ************************************************************************* */
// "Add" two hybrid factors together.
TEST(HybridGaussianFactor, Sum) {
using namespace test_constructor;
DiscreteKey m2(2, 3);
auto A3 = Matrix::Zero(2, 3);
auto f20 = std::make_shared<JacobianFactor>(X(1), A1, X(3), A3, b);
auto f21 = std::make_shared<JacobianFactor>(X(1), A1, X(3), A3, b);
auto f22 = std::make_shared<JacobianFactor>(X(1), A1, X(3), A3, b);
std::vector<GaussianFactorValuePair> factorsA{{f10, 0.0}, {f11, 0.0}};
std::vector<GaussianFactorValuePair> factorsB{
{f20, 0.0}, {f21, 0.0}, {f22, 0.0}};
// TODO(Frank): why specify keys at all? And: keys in factor should be *all*
// keys, deviating from Kevin's scheme. Should we index DT on DiscreteKey?
// Design review!
HybridGaussianFactor hybridFactorA({X(1), X(2)}, {m1}, factorsA);
HybridGaussianFactor hybridFactorB({X(1), X(3)}, {m2}, factorsB);
HybridGaussianFactor hybridFactorA({X(1), X(2)}, m1, {f10, f11});
HybridGaussianFactor hybridFactorB({X(1), X(3)}, m2, {f20, f21, f22});
// Check that number of keys is 3
EXPECT_LONGS_EQUAL(3, hybridFactorA.keys().size());
@ -104,15 +121,8 @@ TEST(HybridGaussianFactor, Sum) {
/* ************************************************************************* */
TEST(HybridGaussianFactor, Printing) {
DiscreteKey m1(1, 2);
auto A1 = Matrix::Zero(2, 1);
auto A2 = Matrix::Zero(2, 2);
auto b = Matrix::Zero(2, 1);
auto f10 = std::make_shared<JacobianFactor>(X(1), A1, X(2), A2, b);
auto f11 = std::make_shared<JacobianFactor>(X(1), A1, X(2), A2, b);
std::vector<GaussianFactorValuePair> factors{{f10, 0.0}, {f11, 0.0}};
HybridGaussianFactor hybridFactor({X(1), X(2)}, {m1}, factors);
using namespace test_constructor;
HybridGaussianFactor hybridFactor({X(1), X(2)}, m1, {f10, f11});
std::string expected =
R"(HybridGaussianFactor
@ -179,9 +189,7 @@ TEST(HybridGaussianFactor, Error) {
auto f0 = std::make_shared<JacobianFactor>(X(1), A01, X(2), A02, b);
auto f1 = std::make_shared<JacobianFactor>(X(1), A11, X(2), A12, b);
std::vector<GaussianFactorValuePair> factors{{f0, 0.0}, {f1, 0.0}};
HybridGaussianFactor hybridFactor({X(1), X(2)}, {m1}, factors);
HybridGaussianFactor hybridFactor({X(1), X(2)}, m1, {f0, f1});
VectorValues continuousValues;
continuousValues.insert(X(1), Vector2(0, 0));

119
gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp
View File

@ -114,6 +114,14 @@ TEST(HybridGaussianFactorGraph, EliminateMultifrontal) {
EXPECT_LONGS_EQUAL(result.first->size(), 1);
EXPECT_LONGS_EQUAL(result.second->size(), 1);
}
/* ************************************************************************* */
namespace two {
std::vector<GaussianFactor::shared_ptr> components(Key key) {
return {std::make_shared<JacobianFactor>(key, I_3x3, Z_3x1),
std::make_shared<JacobianFactor>(key, I_3x3, Vector3::Ones())};
}
} // namespace two
/* ************************************************************************* */
TEST(HybridGaussianFactorGraph, eliminateFullSequentialEqualChance) {
@ -127,10 +135,7 @@ TEST(HybridGaussianFactorGraph, eliminateFullSequentialEqualChance) {
// Add a hybrid gaussian factor ϕ(x1, c1)
DiscreteKey m1(M(1), 2);
DecisionTree<Key, GaussianFactorValuePair> dt(
M(1), {std::make_shared<JacobianFactor>(X(1), I_3x3, Z_3x1), 0.0},
{std::make_shared<JacobianFactor>(X(1), I_3x3, Vector3::Ones()), 0.0});
hfg.add(HybridGaussianFactor({X(1)}, {m1}, dt));
hfg.add(HybridGaussianFactor({X(1)}, m1, two::components(X(1))));
auto result = hfg.eliminateSequential();
@ -153,10 +158,7 @@ TEST(HybridGaussianFactorGraph, eliminateFullSequentialSimple) {
// Add factor between x0 and x1
hfg.add(JacobianFactor(X(0), I_3x3, X(1), -I_3x3, Z_3x1));
std::vector<GaussianFactorValuePair> factors = {
{std::make_shared<JacobianFactor>(X(1), I_3x3, Z_3x1), 0.0},
{std::make_shared<JacobianFactor>(X(1), I_3x3, Vector3::Ones()), 0.0}};
hfg.add(HybridGaussianFactor({X(1)}, {m1}, factors));
hfg.add(HybridGaussianFactor({X(1)}, m1, two::components(X(1))));
// Discrete probability table for c1
hfg.add(DecisionTreeFactor(m1, {2, 8}));
@ -178,10 +180,7 @@ TEST(HybridGaussianFactorGraph, eliminateFullMultifrontalSimple) {
hfg.add(JacobianFactor(X(0), I_3x3, Z_3x1));
hfg.add(JacobianFactor(X(0), I_3x3, X(1), -I_3x3, Z_3x1));
std::vector<GaussianFactorValuePair> factors = {
{std::make_shared<JacobianFactor>(X(1), I_3x3, Z_3x1), 0.0},
{std::make_shared<JacobianFactor>(X(1), I_3x3, Vector3::Ones()), 0.0}};
hfg.add(HybridGaussianFactor({X(1)}, {M(1), 2}, factors));
hfg.add(HybridGaussianFactor({X(1)}, {M(1), 2}, two::components(X(1))));
hfg.add(DecisionTreeFactor(m1, {2, 8}));
// TODO(Varun) Adding extra discrete variable not connected to continuous
@ -207,13 +206,8 @@ TEST(HybridGaussianFactorGraph, eliminateFullMultifrontalCLG) {
// Factor between x0-x1
hfg.add(JacobianFactor(X(0), I_3x3, X(1), -I_3x3, Z_3x1));
// Decision tree with different modes on x1
DecisionTree<Key, GaussianFactorValuePair> dt(
M(1), {std::make_shared<JacobianFactor>(X(1), I_3x3, Z_3x1), 0.0},
{std::make_shared<JacobianFactor>(X(1), I_3x3, Vector3::Ones()), 0.0});
// Hybrid factor P(x1|c1)
hfg.add(HybridGaussianFactor({X(1)}, {m}, dt));
hfg.add(HybridGaussianFactor({X(1)}, m, two::components(X(1))));
// Prior factor on c1
hfg.add(DecisionTreeFactor(m, {2, 8}));
@ -238,16 +232,8 @@ TEST(HybridGaussianFactorGraph, eliminateFullMultifrontalTwoClique) {
hfg.add(JacobianFactor(X(1), I_3x3, X(2), -I_3x3, Z_3x1));
{
std::vector<GaussianFactorValuePair> factors = {
{std::make_shared<JacobianFactor>(X(0), I_3x3, Z_3x1), 0.0},
{std::make_shared<JacobianFactor>(X(0), I_3x3, Vector3::Ones()), 0.0}};
hfg.add(HybridGaussianFactor({X(0)}, {M(0), 2}, factors));
DecisionTree<Key, GaussianFactorValuePair> dt1(
M(1), {std::make_shared<JacobianFactor>(X(2), I_3x3, Z_3x1), 0.0},
{std::make_shared<JacobianFactor>(X(2), I_3x3, Vector3::Ones()), 0.0});
hfg.add(HybridGaussianFactor({X(2)}, {{M(1), 2}}, dt1));
hfg.add(HybridGaussianFactor({X(0)}, {M(0), 2}, two::components(X(0))));
hfg.add(HybridGaussianFactor({X(2)}, {M(1), 2}, two::components(X(2))));
}
hfg.add(DecisionTreeFactor({{M(1), 2}, {M(2), 2}}, "1 2 3 4"));
@ -256,17 +242,8 @@ TEST(HybridGaussianFactorGraph, eliminateFullMultifrontalTwoClique) {
hfg.add(JacobianFactor(X(4), I_3x3, X(5), -I_3x3, Z_3x1));
{
DecisionTree<Key, GaussianFactorValuePair> dt(
M(3), {std::make_shared<JacobianFactor>(X(3), I_3x3, Z_3x1), 0.0},
{std::make_shared<JacobianFactor>(X(3), I_3x3, Vector3::Ones()), 0.0});
hfg.add(HybridGaussianFactor({X(3)}, {{M(3), 2}}, dt));
DecisionTree<Key, GaussianFactorValuePair> dt1(
M(2), {std::make_shared<JacobianFactor>(X(5), I_3x3, Z_3x1), 0.0},
{std::make_shared<JacobianFactor>(X(5), I_3x3, Vector3::Ones()), 0.0});
hfg.add(HybridGaussianFactor({X(5)}, {{M(2), 2}}, dt1));
hfg.add(HybridGaussianFactor({X(3)}, {M(3), 2}, two::components(X(3))));
hfg.add(HybridGaussianFactor({X(5)}, {M(2), 2}, two::components(X(5))));
}
auto ordering_full =
@ -279,7 +256,7 @@ TEST(HybridGaussianFactorGraph, eliminateFullMultifrontalTwoClique) {
EXPECT_LONGS_EQUAL(0, remaining->size());
/*
(Fan) Explanation: the Junction tree will need to reeliminate to get to the
(Fan) Explanation: the Junction tree will need to re-eliminate to get to the
marginal on X(1), which is not possible because it involves eliminating
discrete before continuous. The solution to this, however, is in Murphy02.
TLDR is that this is 1. expensive and 2. inexact. nevertheless it is doable.
@ -551,12 +528,7 @@ TEST(HybridGaussianFactorGraph, optimize) {
hfg.add(JacobianFactor(X(0), I_3x3, Z_3x1));
hfg.add(JacobianFactor(X(0), I_3x3, X(1), -I_3x3, Z_3x1));
DecisionTree<Key, GaussianFactorValuePair> dt(
C(1), {std::make_shared<JacobianFactor>(X(1), I_3x3, Z_3x1), 0.0},
{std::make_shared<JacobianFactor>(X(1), I_3x3, Vector3::Ones()), 0.0});
hfg.add(HybridGaussianFactor({X(1)}, {c1}, dt));
hfg.add(HybridGaussianFactor({X(1)}, c1, two::components(X(1))));
auto result = hfg.eliminateSequential();
@ -642,13 +614,13 @@ TEST(HybridGaussianFactorGraph, ErrorAndProbPrimeTree) {
// regression
EXPECT(assert_equal(expected_error, error_tree, 1e-7));
auto probs = graph.probPrime(delta.continuous());
auto probabilities = graph.probPrime(delta.continuous());
std::vector<double> prob_leaves = {0.36793249, 0.61247742, 0.59489556,
0.99029064};
AlgebraicDecisionTree<Key> expected_probs(discrete_keys, prob_leaves);
AlgebraicDecisionTree<Key> expected_probabilities(discrete_keys, prob_leaves);
// regression
EXPECT(assert_equal(expected_probs, probs, 1e-7));
EXPECT(assert_equal(expected_probabilities, probabilities, 1e-7));
}
/* ****************************************************************************/
@ -706,6 +678,55 @@ TEST(HybridGaussianFactorGraph, ErrorTreeWithConditional) {
EXPECT(assert_equal(expected, errorTree, 1e-9));
}
/* ****************************************************************************/
// Test hybrid gaussian factor graph errorTree during
// incremental operation
TEST(HybridGaussianFactorGraph, IncrementalErrorTree) {
Switching s(4);
HybridGaussianFactorGraph graph;
graph.push_back(s.linearizedFactorGraph.at(0)); // f(X0)
graph.push_back(s.linearizedFactorGraph.at(1)); // f(X0, X1, M0)
graph.push_back(s.linearizedFactorGraph.at(2)); // f(X1, X2, M1)
graph.push_back(s.linearizedFactorGraph.at(4)); // f(X1)
graph.push_back(s.linearizedFactorGraph.at(5)); // f(X2)
graph.push_back(s.linearizedFactorGraph.at(7)); // f(M0)
graph.push_back(s.linearizedFactorGraph.at(8)); // f(M0, M1)
HybridBayesNet::shared_ptr hybridBayesNet = graph.eliminateSequential();
EXPECT_LONGS_EQUAL(5, hybridBayesNet->size());
HybridValues delta = hybridBayesNet->optimize();
auto error_tree = graph.errorTree(delta.continuous());
std::vector<DiscreteKey> discrete_keys = {{M(0), 2}, {M(1), 2}};
std::vector<double> leaves = {0.99985581, 0.4902432, 0.51936941,
0.0097568009};
AlgebraicDecisionTree<Key> expected_error(discrete_keys, leaves);
// regression
EXPECT(assert_equal(expected_error, error_tree, 1e-7));
graph = HybridGaussianFactorGraph();
graph.push_back(*hybridBayesNet);
graph.push_back(s.linearizedFactorGraph.at(3)); // f(X2, X3, M2)
graph.push_back(s.linearizedFactorGraph.at(6)); // f(X3)
hybridBayesNet = graph.eliminateSequential();
EXPECT_LONGS_EQUAL(7, hybridBayesNet->size());
delta = hybridBayesNet->optimize();
auto error_tree2 = graph.errorTree(delta.continuous());
discrete_keys = {{M(0), 2}, {M(1), 2}, {M(2), 2}};
leaves = {0.50985198, 0.0097577296, 0.50009425, 0,
0.52922138, 0.029127133, 0.50985105, 0.0097567964};
AlgebraicDecisionTree<Key> expected_error2(discrete_keys, leaves);
// regression
EXPECT(assert_equal(expected_error, error_tree, 1e-7));
}
/* ****************************************************************************/
// Check that assembleGraphTree assembles Gaussian factor graphs for each
// assignment.

85
python/gtsam/examples/CameraResectioning.py Normal file
View File

@ -0,0 +1,85 @@
# pylint: disable=consider-using-from-import,invalid-name,no-name-in-module,no-member,missing-function-docstring
"""
This is a 1:1 transcription of CameraResectioning.cpp.
"""
import numpy as np
from gtsam import Cal3_S2, CustomFactor, LevenbergMarquardtOptimizer, KeyVector
from gtsam import NonlinearFactor, NonlinearFactorGraph
from gtsam import PinholeCameraCal3_S2, Point2, Point3, Pose3, Rot3, Values
from gtsam.noiseModel import Base as SharedNoiseModel, Diagonal
from gtsam.symbol_shorthand import X
def resectioning_factor(
model: SharedNoiseModel,
key: int,
calib: Cal3_S2,
p: Point2,
P: Point3,
) -> NonlinearFactor:
def error_func(this: CustomFactor, v: Values, H: list[np.ndarray]) -> np.ndarray:
pose = v.atPose3(this.keys()[0])
camera = PinholeCameraCal3_S2(pose, calib)
if H is None:
return camera.project(P) - p
Dpose = np.zeros((2, 6), order="F")
Dpoint = np.zeros((2, 3), order="F")
Dcal = np.zeros((2, 5), order="F")
result = camera.project(P, Dpose, Dpoint, Dcal) - p
H[0] = Dpose
return result
return CustomFactor(model, KeyVector([key]), error_func)
def main() -> None:
"""
Camera: f = 1, Image: 100x100, center: 50, 50.0
Pose (ground truth): (Xw, -Yw, -Zw, [0,0,2.0]')
Known landmarks:
3D Points: (10,10,0) (-10,10,0) (-10,-10,0) (10,-10,0)
Perfect measurements:
2D Point: (55,45) (45,45) (45,55) (55,55)
"""
# read camera intrinsic parameters
calib = Cal3_S2(1, 1, 0, 50, 50)
# 1. create graph
graph = NonlinearFactorGraph()
# 2. add factors to the graph
measurement_noise = Diagonal.Sigmas(np.array([0.5, 0.5]))
graph.add(
resectioning_factor(
measurement_noise, X(1), calib, Point2(55, 45), Point3(10, 10, 0)
)
)
graph.add(
resectioning_factor(
measurement_noise, X(1), calib, Point2(45, 45), Point3(-10, 10, 0)
)
)
graph.add(
resectioning_factor(
measurement_noise, X(1), calib, Point2(45, 55), Point3(-10, -10, 0)
)
)
graph.add(
resectioning_factor(
measurement_noise, X(1), calib, Point2(55, 55), Point3(10, -10, 0)
)
)
# 3. Create an initial estimate for the camera pose
initial: Values = Values()
initial.insert(X(1), Pose3(Rot3(1, 0, 0, 0, -1, 0, 0, 0, -1), Point3(0, 0, 1)))
# 4. Optimize the graph using Levenberg-Marquardt
result: Values = LevenbergMarquardtOptimizer(graph, initial).optimize()
result.print("Final result:\n")
if __name__ == "__main__":
main()