LinearContainerFactor now includes ability to "relinearize" when supplied with an original linearization point

gtsam_unstable/nonlinear/LinearContainerFactor.cpp

@@ -140,7 +140,7 @@ double LinearContainerFactor::error(const Values& c) const {
   if (!linearizationPoint_)
     return 0;
 
-  // Extract subset of values for comparision
+  // Extract subset of values for comparison
   Values csub;
   BOOST_FOREACH(const gtsam::Key& key, keys())
     csub.insert(key, c.at(key));
@@ -185,7 +185,39 @@ GaussianFactor::shared_ptr LinearContainerFactor::order(const Ordering& ordering
 /* ************************************************************************* */
 GaussianFactor::shared_ptr LinearContainerFactor::linearize(
     const Values& c, const Ordering& ordering) const {
+  if (!hasLinearizationPoint())
     return order(ordering);
+
+  // Extract subset of values
+  Values subsetC;
+  BOOST_FOREACH(const gtsam::Key& key, this->keys())
+    subsetC.insert(key, c.at(key));
+
+  // Create a temp ordering for this factor
+  Ordering localOrdering = *subsetC.orderingArbitrary();
+
+  // Determine delta between linearization points using new ordering
+  VectorValues delta = linearizationPoint_->localCoordinates(subsetC, localOrdering);
+
+  // clone and reorder linear factor to final ordering
+  GaussianFactor::shared_ptr linFactor = order(localOrdering);
+  if (isJacobian()) {
+    JacobianFactor::shared_ptr jacFactor = boost::shared_dynamic_cast<JacobianFactor>(linFactor);
+    jacFactor->getb() += jacFactor->unweighted_error(delta) + jacFactor->getb();
+  } else {
+    HessianFactor::shared_ptr hesFactor = boost::shared_dynamic_cast<HessianFactor>(linFactor);
+    size_t dim = hesFactor->linearTerm().size();
+    Eigen::Block<HessianFactor::Block> Gview = hesFactor->info().block(0, 0, dim, dim);
+    Vector G_delta = Gview.selfadjointView<Eigen::Upper>() * delta.vector();
+    hesFactor->constantTerm() += delta.vector().dot(G_delta) + delta.vector().dot(hesFactor->linearTerm());
+    hesFactor->linearTerm() += G_delta;
+  }
+
+  // reset ordering
+  Ordering::InvertedMap invLocalOrdering = localOrdering.invert();
+  BOOST_FOREACH(Index& idx, linFactor->keys())
+    idx = ordering[invLocalOrdering[idx] ];
+  return linFactor;
 }
 
 /* ************************************************************************* */
@@ -193,6 +225,11 @@ bool LinearContainerFactor::isJacobian() const {
   return boost::shared_dynamic_cast<JacobianFactor>(factor_);
 }
 
+/* ************************************************************************* */
+bool LinearContainerFactor::isHessian() const {
+  return boost::shared_dynamic_cast<HessianFactor>(factor_);
+}
+
 /* ************************************************************************* */
 JacobianFactor::shared_ptr LinearContainerFactor::toJacobian() const {
   return boost::shared_dynamic_cast<JacobianFactor>(factor_);

gtsam_unstable/nonlinear/LinearContainerFactor.h

@@ -114,10 +114,13 @@ public:
 
   // casting syntactic sugar
 
+  inline bool hasLinearizationPoint() const { return linearizationPoint_; }
+
   /**
-   * Simple check whether this is a Jacobian or Hessian factor
+   * Simple checks whether this is a Jacobian or Hessian factor
    */
   bool isJacobian() const;
+  bool isHessian() const;
 
   /** Casts to JacobianFactor */
   JacobianFactor::shared_ptr toJacobian() const;

gtsam_unstable/nonlinear/tests/testLinearContainerFactor.cpp

@@ -7,11 +7,15 @@
 
 #include <CppUnitLite/TestHarness.h>
 
+#include <boost/assign/std/vector.hpp>
+
 #include <gtsam_unstable/nonlinear/LinearContainerFactor.h>
 
 #include <gtsam/base/TestableAssertions.h>
 #include <gtsam/geometry/Pose2.h>
 
+using namespace std;
+using namespace boost::assign;
 using namespace gtsam;
 
 const gtsam::noiseModel::Diagonal::shared_ptr diag_model2 = noiseModel::Diagonal::Sigmas(Vector_(2, 1.0, 1.0));
@@ -27,20 +31,26 @@ TEST( testLinearContainerFactor, generic_jacobian_factor ) {
 
   Ordering initOrdering; initOrdering += x1, x2, l1, l2;
 
-  JacobianFactor expLinFactor1(
+  Matrix A1 = Matrix_(2,2,
-      initOrdering[l1],
-      Matrix_(2,2,
       2.74222, -0.0067457,
-               0.0,  2.63624),
+      0.0,  2.63624);
-      initOrdering[l2],
+  Matrix A2 = Matrix_(2,2,
-      Matrix_(2,2,
       -0.0455167, -0.0443573,
-          -0.0222154, -0.102489),
+      -0.0222154, -0.102489);
-      Vector_(2, 0.0277052,
+  Vector b = Vector_(2, 0.0277052,
-                 -0.0533393),
+      -0.0533393);
-      diag_model2);
+
+  JacobianFactor expLinFactor(initOrdering[l1], A1, initOrdering[l2], A2, b, diag_model2);
+
+  LinearContainerFactor actFactor(expLinFactor, initOrdering);
+  EXPECT_LONGS_EQUAL(2, actFactor.size());
+  EXPECT(actFactor.isJacobian());
+  EXPECT(!actFactor.isHessian());
+
+  // check keys
+  std::vector<gtsam::Key> expKeys; expKeys += l1, l2;
+  EXPECT(assert_container_equality(expKeys, actFactor.keys()));
 
-  LinearContainerFactor actFactor1(expLinFactor1, initOrdering);
   Values values;
   values.insert(l1, landmark1);
   values.insert(l2, landmark2);
@@ -48,26 +58,13 @@ TEST( testLinearContainerFactor, generic_jacobian_factor ) {
   values.insert(x2, poseA2);
 
   // Check reconstruction from same ordering
-  GaussianFactor::shared_ptr actLinearizationA = actFactor1.linearize(values, initOrdering);
+  GaussianFactor::shared_ptr actLinearizationA = actFactor.linearize(values, initOrdering);
-  EXPECT(assert_equal(*expLinFactor1.clone(), *actLinearizationA, tol));
+  EXPECT(assert_equal(*expLinFactor.clone(), *actLinearizationA, tol));
 
   // Check reconstruction from new ordering
   Ordering newOrdering; newOrdering += x1, l1, x2, l2;
-  GaussianFactor::shared_ptr actLinearizationB = actFactor1.linearize(values, newOrdering);
+  GaussianFactor::shared_ptr actLinearizationB = actFactor.linearize(values, newOrdering);
-
+  JacobianFactor expLinFactor2(newOrdering[l1], A1, newOrdering[l2], A2, b, diag_model2);
-  JacobianFactor expLinFactor2(
-      newOrdering[l1],
-      Matrix_(2,2,
-           2.74222, -0.0067457,
-               0.0,  2.63624),
-      newOrdering[l2],
-      Matrix_(2,2,
-          -0.0455167, -0.0443573,
-          -0.0222154, -0.102489),
-      Vector_(2, 0.0277052,
-                 -0.0533393),
-      diag_model2);
-
   EXPECT(assert_equal(*expLinFactor2.clone(), *actLinearizationB, tol));
 }
 
@@ -76,18 +73,16 @@ TEST( testLinearContainerFactor, jacobian_factor_withlinpoints ) {
 
   Ordering ordering; ordering += x1, x2, l1, l2;
 
-  JacobianFactor expLinFactor(
+  Matrix A1 = Matrix_(2,2,
-      ordering[l1],
-      Matrix_(2,2,
       2.74222, -0.0067457,
-               0.0,  2.63624),
+      0.0,  2.63624);
-      ordering[l2],
+  Matrix A2 = Matrix_(2,2,
-      Matrix_(2,2,
       -0.0455167, -0.0443573,
-          -0.0222154, -0.102489),
+      -0.0222154, -0.102489);
-      Vector_(2, 0.0277052,
+  Vector b = Vector_(2, 0.0277052,
-                 -0.0533393),
+      -0.0533393);
-      diag_model2);
+
+  JacobianFactor expLinFactor(ordering[l1], A1, ordering[l2], A2, b, diag_model2);
 
   Values values;
   values.insert(l1, landmark1);
@@ -107,16 +102,27 @@ TEST( testLinearContainerFactor, jacobian_factor_withlinpoints ) {
   expLinPoint.insert(l1, landmark1);
   expLinPoint.insert(l2, landmark2);
   CHECK(actFactor.linearizationPoint());
+  EXPECT(actFactor.hasLinearizationPoint());
   EXPECT(assert_equal(expLinPoint, *actFactor.linearizationPoint()));
 
   // Check error evaluation
+  Vector delta_l1 = Vector_(2, 1.0, 2.0);
+  Vector delta_l2 = Vector_(2, 3.0, 4.0);
+
   VectorValues delta = values.zeroVectors(ordering);
-  delta.at(ordering[l1]) = Vector_(2, 1.0, 2.0);
+  delta.at(ordering[l1]) = delta_l1;
-  delta.at(ordering[l2]) = Vector_(2, 3.0, 4.0);
+  delta.at(ordering[l2]) = delta_l2;
   Values noisyValues = values.retract(delta, ordering);
   double expError = expLinFactor.error(delta);
   EXPECT_DOUBLES_EQUAL(expError, actFactor.error(noisyValues), tol);
   EXPECT_DOUBLES_EQUAL(expLinFactor.error(values.zeroVectors(ordering)), actFactor.error(values), tol);
+
+  // Check linearization with corrections for updated linearization point
+  Ordering newOrdering; newOrdering += x1, l1, x2, l2;
+  GaussianFactor::shared_ptr actLinearizationB = actFactor.linearize(noisyValues, newOrdering);
+  Vector bprime = b + A1 * delta_l1 + A2 * delta_l2;
+  JacobianFactor expLinFactor2(newOrdering[l1], A1, newOrdering[l2], A2, bprime, diag_model2);
+  EXPECT(assert_equal(*expLinFactor2.clone(), *actLinearizationB, tol));
 }
 
 /* ************************************************************************* */
@@ -125,10 +131,14 @@ TEST( testLinearContainerFactor, generic_hessian_factor ) {
   Matrix G12 = Matrix_(1,2, 2.0, 4.0);
   Matrix G13 = Matrix_(1,3, 3.0, 6.0, 9.0);
 
-  Matrix G22 = Matrix_(2,2, 3.0, 5.0, 0.0, 6.0);
+  Matrix G22 = Matrix_(2,2, 3.0, 5.0,
-  Matrix G23 = Matrix_(2,3, 4.0, 6.0, 8.0, 1.0, 2.0, 4.0);
+                            0.0, 6.0);
+  Matrix G23 = Matrix_(2,3, 4.0, 6.0, 8.0,
+                            1.0, 2.0, 4.0);
 
-  Matrix G33 = Matrix_(3,3, 1.0, 2.0, 3.0, 0.0, 5.0, 6.0, 0.0, 0.0, 9.0);
+  Matrix G33 = Matrix_(3,3, 1.0, 2.0, 3.0,
+                            0.0, 5.0, 6.0,
+                            0.0, 0.0, 9.0);
 
   Vector g1 = Vector_(1, -7.0);
   Vector g2 = Vector_(2, -8.0, -9.0);
@@ -147,6 +157,9 @@ TEST( testLinearContainerFactor, generic_hessian_factor ) {
   values.insert(x2, poseA2);
 
   LinearContainerFactor actFactor(initFactor, initOrdering);
+  EXPECT(!actFactor.isJacobian());
+  EXPECT(actFactor.isHessian());
+
   GaussianFactor::shared_ptr actLinearization1 = actFactor.linearize(values, initOrdering);
   EXPECT(assert_equal(*initFactor.clone(), *actLinearization1, tol));
 
@@ -157,6 +170,116 @@ TEST( testLinearContainerFactor, generic_hessian_factor ) {
    EXPECT(assert_equal(*expLinFactor.clone(), *actLinearization2, tol));
 }
 
+/* ************************************************************************* */
+TEST( testLinearContainerFactor, hessian_factor_withlinpoints ) {
+  // 2 variable example, one pose, one landmark (planar)
+  // Initial ordering: x1, l1
+
+  Matrix G11 = Matrix_(3,3,
+      1.0, 2.0, 3.0,
+      0.0, 5.0, 6.0,
+      0.0, 0.0, 9.0);
+  Matrix G12 = Matrix_(3,2,
+      1.0, 2.0,
+      3.0, 5.0,
+      4.0, 6.0);
+  Vector g1 = Vector_(3,  1.0,  2.0,  3.0);
+
+  Matrix G22 = Matrix_(2,2,
+        0.5, 0.2,
+        0.0, 0.6);
+
+  Vector g2 = Vector_(2, -8.0, -9.0);
+
+  double f = 10.0;
+
+  // Construct full matrices
+  Matrix G(5,5);
+  G << G11, G12, Matrix::Zero(2,3), G22;
+
+  Ordering ordering; ordering += x1, x2, l1;
+  HessianFactor initFactor(ordering[x1], ordering[l1], G11, G12, g1, G22, g2, f);
+
+  Values linearizationPoint, expLinPoints;
+  linearizationPoint.insert(l1, landmark1);
+  linearizationPoint.insert(x1, poseA1);
+  expLinPoints = linearizationPoint;
+  linearizationPoint.insert(x2, poseA2);
+
+  LinearContainerFactor actFactor(initFactor, ordering, linearizationPoint);
+  EXPECT(!actFactor.isJacobian());
+  EXPECT(actFactor.isHessian());
+
+  EXPECT(actFactor.hasLinearizationPoint());
+  Values actLinPoint = *actFactor.linearizationPoint();
+  EXPECT(assert_equal(expLinPoints, actLinPoint));
+
+  // Create delta
+  Vector delta_l1 = Vector_(2, 1.0, 2.0);
+  Vector delta_x1 = Vector_(3, 3.0, 4.0, 0.5);
+  Vector delta_x2 = Vector_(3, 6.0, 7.0, 0.3);
+
+  // Check error calculation
+  VectorValues delta = linearizationPoint.zeroVectors(ordering);
+  delta.at(ordering[l1]) = delta_l1;
+  delta.at(ordering[x1]) = delta_x1;
+  delta.at(ordering[x2]) = delta_x2;
+  EXPECT(assert_equal(Vector_(5, 3.0, 4.0, 0.5, 1.0, 2.0), delta.vector(initFactor.keys())));
+  Values noisyValues = linearizationPoint.retract(delta, ordering);
+
+  double expError = initFactor.error(delta);
+  EXPECT_DOUBLES_EQUAL(expError, actFactor.error(noisyValues), tol);
+  EXPECT_DOUBLES_EQUAL(initFactor.error(linearizationPoint.zeroVectors(ordering)), actFactor.error(linearizationPoint), tol);
+
+  // Compute updated versions
+  Vector dv = Vector_(5, 3.0, 4.0, 0.5, 1.0, 2.0);
+  Vector g(5); g << g1, g2;
+  Vector g_prime = G.selfadjointView<Eigen::Upper>() * dv + g;
+
+  // Check linearization with corrections for updated linearization point
+  Vector g1_prime = g_prime.head(3);
+  Vector g2_prime = g_prime.tail(2);
+  double f_prime = f + dv.transpose() * G.selfadjointView<Eigen::Upper>() * dv + dv.transpose() * g;
+  HessianFactor expNewFactor(ordering[x1], ordering[l1], G11, G12, g1_prime, G22, g2_prime, f_prime);
+  EXPECT(assert_equal(*expNewFactor.clone(), *actFactor.linearize(noisyValues, ordering), tol));
+}
+
+/* ************************************************************************* */
+TEST( testLinearContainerFactor, creation ) {
+  // Create a set of local keys (No robot label)
+  Key  l1 = 11, l2 = 12,
+      l3 = 13, l4 = 14,
+      l5 = 15, l6 = 16,
+      l7 = 17, l8 = 18;
+
+  // creating an ordering to decode the linearized factor
+  Ordering ordering;
+  ordering += l1,l2,l3,l4,l5,l6,l7,l8;
+
+  // create a linear factor
+  SharedDiagonal model = noiseModel::Unit::Create(2);
+  JacobianFactor::shared_ptr linear_factor(new JacobianFactor(
+      ordering[l3], eye(2,2), ordering[l5], 2.0 * eye(2,2), zero(2), model));
+
+  // create a set of values - build with full set of values
+  gtsam::Values full_values, exp_values;
+  full_values.insert(l3, Point2(1.0, 2.0));
+  full_values.insert(l5, Point2(4.0, 3.0));
+  exp_values = full_values;
+  full_values.insert(l1, Point2(3.0, 7.0));
+
+  LinearContainerFactor actual(linear_factor, ordering, full_values);
+
+  // Verify the keys
+  std::vector<gtsam::Key> expKeys;
+  expKeys += l3, l5;
+  EXPECT(assert_container_equality(expKeys, actual.keys()));
+
+  // Verify subset of linearization points
+  EXPECT(assert_equal(exp_values, actual.linearizationPoint(), tol));
+}
+
 /* ************************************************************************* */
 int main() { TestResult tr; return TestRegistry::runAllTests(tr); }
 /* ************************************************************************* */
+