test for differing covariances

gtsam/hybrid/tests/testMixtureFactor.cpp

@@ -18,6 +18,9 @@
 
 #include <gtsam/base/TestableAssertions.h>
 #include <gtsam/discrete/DiscreteValues.h>
+#include <gtsam/hybrid/HybridBayesNet.h>
+#include <gtsam/hybrid/HybridGaussianFactorGraph.h>
+#include <gtsam/hybrid/HybridNonlinearFactorGraph.h>
 #include <gtsam/hybrid/MixtureFactor.h>
 #include <gtsam/inference/Symbol.h>
 #include <gtsam/slam/BetweenFactor.h>
@@ -114,6 +117,73 @@ TEST(MixtureFactor, Dim) {
   EXPECT_LONGS_EQUAL(1, mixtureFactor.dim());
 }
 
+/* ************************************************************************* */
+// 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 bn2;
+  bn2.emplace_back(gm);
+
+  gtsam::VectorValues measurements;
+  measurements.insert(Z(1), gtsam::Z_1x1);
+  // Create FG with single GaussianMixtureFactor
+  auto mixture_fg = bn2.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();
+  // hbn->print("\n\nfinal bayes net");
+
+  HybridValues actual_values = hbn->optimize();
+
+  VectorValues cv;
+  cv.insert(X(1), Vector1(0.0));
+  cv.insert(X(2), Vector1(0.0));
+  DiscreteValues dv;
+  dv.insert({M(1), 1});
+  HybridValues expected_values(cv, dv);
+
+  EXPECT(assert_equal(expected_values, actual_values));
+}
+
 /* ************************************************************************* */
 int main() {
   TestResult tr;