Merge pull request #1301 from borglab/hybrid/gaussian-conditional

gtsam/hybrid/HybridGaussianFactorGraph.cpp

@@ -96,8 +96,12 @@ GaussianMixtureFactor::Sum sumFrontals(
       }
 
     } else if (f->isContinuous()) {
-      deferredFactors.push_back(
+      if (auto gf = boost::dynamic_pointer_cast<HybridGaussianFactor>(f)) {
-          boost::dynamic_pointer_cast<HybridGaussianFactor>(f)->inner());
+        deferredFactors.push_back(gf->inner());
+      }
+      if (auto cg = boost::dynamic_pointer_cast<HybridConditional>(f)) {
+        deferredFactors.push_back(cg->asGaussian());
+      }
 
     } else if (f->isDiscrete()) {
       // Don't do anything for discrete-only factors

gtsam/hybrid/tests/Switching.h

@@ -115,7 +115,6 @@ inline std::pair<KeyVector, std::vector<int>> makeBinaryOrdering(
 /* ***************************************************************************
  */
 using MotionModel = BetweenFactor<double>;
-// using MotionMixture = MixtureFactor<MotionModel>;
 
 // Test fixture with switching network.
 struct Switching {
@@ -125,7 +124,13 @@ struct Switching {
   HybridGaussianFactorGraph linearizedFactorGraph;
   Values linearizationPoint;
 
-  /// Create with given number of time steps.
+  /**
+   * @brief Create with given number of time steps.
+   *
+   * @param K The total number of timesteps.
+   * @param between_sigma The stddev between poses.
+   * @param prior_sigma The stddev on priors (also used for measurements).
+   */
   Switching(size_t K, double between_sigma = 1.0, double prior_sigma = 0.1)
       : K(K) {
     // Create DiscreteKeys for binary K modes, modes[0] will not be used.
@@ -166,6 +171,8 @@ struct Switching {
       linearizationPoint.insert<double>(X(k), static_cast<double>(k));
     }
 
+    // The ground truth is robot moving forward
+    // and one less than the linearization point
     linearizedFactorGraph = *nonlinearFactorGraph.linearize(linearizationPoint);
   }
 

gtsam/hybrid/tests/testHybridGaussianFactorGraph.cpp

@@ -500,6 +500,7 @@ TEST(HybridGaussianFactorGraph, SwitchingTwoVar) {
   }
 }
 
+/* ************************************************************************* */
 TEST(HybridGaussianFactorGraph, optimize) {
   HybridGaussianFactorGraph hfg;
 
@@ -521,6 +522,46 @@ TEST(HybridGaussianFactorGraph, optimize) {
 
   EXPECT(assert_equal(hv.atDiscrete(C(1)), int(0)));
 }
+
+/* ************************************************************************* */
+// Test adding of gaussian conditional and re-elimination.
+TEST(HybridGaussianFactorGraph, Conditionals) {
+  Switching switching(4);
+  HybridGaussianFactorGraph hfg;
+
+  hfg.push_back(switching.linearizedFactorGraph.at(0));  // P(X1)
+  Ordering ordering;
+  ordering.push_back(X(1));
+  HybridBayesNet::shared_ptr bayes_net = hfg.eliminateSequential(ordering);
+
+  hfg.push_back(switching.linearizedFactorGraph.at(1));  // P(X1, X2 | M1)
+  hfg.push_back(*bayes_net);
+  hfg.push_back(switching.linearizedFactorGraph.at(2));  // P(X2, X3 | M2)
+  hfg.push_back(switching.linearizedFactorGraph.at(5));  // P(M1)
+  ordering.push_back(X(2));
+  ordering.push_back(X(3));
+  ordering.push_back(M(1));
+  ordering.push_back(M(2));
+
+  bayes_net = hfg.eliminateSequential(ordering);
+
+  HybridValues result = bayes_net->optimize();
+
+  Values expected_continuous;
+  expected_continuous.insert<double>(X(1), 0);
+  expected_continuous.insert<double>(X(2), 1);
+  expected_continuous.insert<double>(X(3), 2);
+  expected_continuous.insert<double>(X(4), 4);
+  Values result_continuous =
+      switching.linearizationPoint.retract(result.continuous());
+  EXPECT(assert_equal(expected_continuous, result_continuous));
+
+  DiscreteValues expected_discrete;
+  expected_discrete[M(1)] = 1;
+  expected_discrete[M(2)] = 1;
+  EXPECT(assert_equal(expected_discrete, result.discrete()));
+}
+
 /* ************************************************************************* */
 int main() {
   TestResult tr;