cleanup

gtsam/slam/tests/testOrientedPlane3Factor.cpp

@@ -36,89 +36,86 @@ using namespace std;
 GTSAM_CONCEPT_TESTABLE_INST(OrientedPlane3)
 GTSAM_CONCEPT_MANIFOLD_INST(OrientedPlane3)
 
+using symbol_shorthand::P;  //< Planes
+using symbol_shorthand::X;  //< Pose3
+
 // *************************************************************************
-TEST (OrientedPlane3Factor, lm_translation_error) {
+TEST(OrientedPlane3Factor, lm_translation_error) {
   // Tests one pose, two measurements of the landmark that differ in range only.
   // Normal along -x, 3m away
-  Symbol lm_sym('p', 0);
   OrientedPlane3 test_lm0(-1.0, 0.0, 0.0, 3.0);
 
-  ISAM2 isam2;
+  NonlinearFactorGraph graph;
-  Values new_values;
-  NonlinearFactorGraph new_graph;
 
-  // Init pose and prior.  Pose Prior is needed since a single plane measurement does not fully constrain the pose
+  // Init pose and prior.  Pose Prior is needed since a single plane measurement
-  Symbol init_sym('x', 0);
+  // does not fully constrain the pose
   Pose3 init_pose(Rot3::Ypr(0.0, 0.0, 0.0), Point3(0.0, 0.0, 0.0));
-  Vector sigmas(6);
+  Vector6 sigmas;
   sigmas << 0.001, 0.001, 0.001, 0.001, 0.001, 0.001;
-  new_graph.addPrior(
+  graph.addPrior(X(0), init_pose, noiseModel::Diagonal::Sigmas(sigmas));
-      init_sym, init_pose, noiseModel::Diagonal::Sigmas(sigmas));
-  new_values.insert(init_sym, init_pose);
 
   // Add two landmark measurements, differing in range
-  new_values.insert(lm_sym, test_lm0);
+  Vector3 sigmas3 {0.1, 0.1, 0.1};
-  Vector sigmas3(3);
+  Vector4 measurement0 {-1.0, 0.0, 0.0, 3.0};
-  sigmas3 << 0.1, 0.1, 0.1;
+  OrientedPlane3Factor factor0(
-  Vector test_meas0_mean(4);
+      measurement0, noiseModel::Diagonal::Sigmas(sigmas3), X(0), P(0));
-  test_meas0_mean << -1.0, 0.0, 0.0, 3.0;
+  graph.add(factor0);
-  OrientedPlane3Factor test_meas0(test_meas0_mean,
+  Vector4 measurement1 {-1.0, 0.0, 0.0, 1.0};
-      noiseModel::Diagonal::Sigmas(sigmas3), init_sym, lm_sym);
+  OrientedPlane3Factor factor1(
-  new_graph.add(test_meas0);
+      measurement1, noiseModel::Diagonal::Sigmas(sigmas3), X(0), P(0));
-  Vector test_meas1_mean(4);
+  graph.add(factor1);
-  test_meas1_mean << -1.0, 0.0, 0.0, 1.0;
+
-  OrientedPlane3Factor test_meas1(test_meas1_mean,
+  // Initial Estimate
-      noiseModel::Diagonal::Sigmas(sigmas3), init_sym, lm_sym);
+  Values values;
-  new_graph.add(test_meas1);
+  values.insert(X(0), init_pose);
+  values.insert(P(0), test_lm0);
 
   // Optimize
-  ISAM2Result result = isam2.update(new_graph, new_values);
+  ISAM2 isam2;
+  ISAM2Result result = isam2.update(graph, values);
   Values result_values = isam2.calculateEstimate();
-  OrientedPlane3 optimized_plane_landmark = result_values.at<OrientedPlane3>(
+  OrientedPlane3 optimized_plane_landmark =
-      lm_sym);
+      result_values.at<OrientedPlane3>(P(0));
 
   // Given two noisy measurements of equal weight, expect result between the two
   OrientedPlane3 expected_plane_landmark(-1.0, 0.0, 0.0, 2.0);
   EXPECT(assert_equal(optimized_plane_landmark, expected_plane_landmark));
 }
 
-// *************************************************************************
+// // *************************************************************************
 TEST (OrientedPlane3Factor, lm_rotation_error) {
   // Tests one pose, two measurements of the landmark that differ in angle only.
   // Normal along -x, 3m away
-  Symbol lm_sym('p', 0);
   OrientedPlane3 test_lm0(-1.0/sqrt(1.01), 0.1/sqrt(1.01), 0.0, 3.0);
 
-  ISAM2 isam2;
+  NonlinearFactorGraph graph;
-  Values new_values;
-  NonlinearFactorGraph new_graph;
 
   // Init pose and prior.  Pose Prior is needed since a single plane measurement does not fully constrain the pose
-  Symbol init_sym('x', 0);
   Pose3 init_pose(Rot3::Ypr(0.0, 0.0, 0.0), Point3(0.0, 0.0, 0.0));
-  new_graph.addPrior(init_sym, init_pose,
+  graph.addPrior(X(0), init_pose,
       noiseModel::Diagonal::Sigmas(
           (Vector(6) << 0.001, 0.001, 0.001, 0.001, 0.001, 0.001).finished()));
-  new_values.insert(init_sym, init_pose);
 
-//  // Add two landmark measurements, differing in angle
+  // Add two landmark measurements, differing in angle
-  new_values.insert(lm_sym, test_lm0);
+  Vector4 measurement0 {-1.0, 0.0, 0.0, 3.0};
-  Vector test_meas0_mean(4);
+  OrientedPlane3Factor factor0(measurement0,
-  test_meas0_mean << -1.0, 0.0, 0.0, 3.0;
+      noiseModel::Diagonal::Sigmas(Vector3(0.1, 0.1, 0.1)), X(0), P(0));
-  OrientedPlane3Factor test_meas0(test_meas0_mean,
+  graph.add(factor0);
-      noiseModel::Diagonal::Sigmas(Vector3(0.1, 0.1, 0.1)), init_sym, lm_sym);
+  Vector4 measurement1 {0.0, -1.0, 0.0, 3.0};
-  new_graph.add(test_meas0);
+  OrientedPlane3Factor factor1(measurement1,
-  Vector test_meas1_mean(4);
+      noiseModel::Diagonal::Sigmas(Vector3(0.1, 0.1, 0.1)), X(0), P(0));
-  test_meas1_mean << 0.0, -1.0, 0.0, 3.0;
+  graph.add(factor1);
-  OrientedPlane3Factor test_meas1(test_meas1_mean,
+
-      noiseModel::Diagonal::Sigmas(Vector3(0.1, 0.1, 0.1)), init_sym, lm_sym);
+  // Initial Estimate
-  new_graph.add(test_meas1);
+  Values values;
+  values.insert(X(0), init_pose);
+  values.insert(P(0), test_lm0);
 
   // Optimize
-  ISAM2Result result = isam2.update(new_graph, new_values);
+  ISAM2 isam2;
+  ISAM2Result result = isam2.update(graph, values);
   Values result_values = isam2.calculateEstimate();
-  OrientedPlane3 optimized_plane_landmark = result_values.at<OrientedPlane3>(
+  auto optimized_plane_landmark = result_values.at<OrientedPlane3>(P(0));
-      lm_sym);
 
   // Given two noisy measurements of equal weight, expect result between the two
   OrientedPlane3 expected_plane_landmark(-sqrt(2.0) / 2.0, -sqrt(2.0) / 2.0,
@@ -126,6 +123,7 @@ TEST (OrientedPlane3Factor, lm_rotation_error) {
   EXPECT(assert_equal(optimized_plane_landmark, expected_plane_landmark));
 }
 
+// *************************************************************************
 TEST( OrientedPlane3Factor, Derivatives ) {
   // Measurement
   OrientedPlane3 p(sqrt(2)/2, -sqrt(2)/2, 0, 5);
@@ -163,7 +161,7 @@ TEST( OrientedPlane3DirectionPrior, Constructor ) {
   // If pitch and roll are zero for an aerospace frame,
   // that means Z is pointing down, i.e., direction of Z = (0,0,-1)
 
-  Vector planeOrientation = (Vector(4) << 0.0, 0.0, -1.0, 10.0).finished(); // all vertical planes directly facing the origin
+  Vector4 planeOrientation = (Vector(4) << 0.0, 0.0, -1.0, 10.0).finished(); // all vertical planes directly facing the origin
 
   // Factor
   Key key(1);
@@ -171,9 +169,9 @@ TEST( OrientedPlane3DirectionPrior, Constructor ) {
   OrientedPlane3DirectionPrior factor(key, planeOrientation, model);
 
   // Create a linearization point at the zero-error point
-  Vector theta1 = Vector4(0.0, 0.02, -1.2, 10.0);
+  Vector4 theta1 {0.0, 0.02, -1.2, 10.0};
-  Vector theta2 = Vector4(0.0, 0.1, -0.8, 10.0);
+  Vector4 theta2 {0.0, 0.1, -0.8, 10.0};
-  Vector theta3 = Vector4(0.0, 0.2, -0.9, 10.0);
+  Vector4 theta3 {0.0, 0.2, -0.9, 10.0};
 
   OrientedPlane3 T1(theta1);
   OrientedPlane3 T2(theta2);
@@ -204,90 +202,18 @@ TEST( OrientedPlane3DirectionPrior, Constructor ) {
   EXPECT(assert_equal(expectedH3, actualH3, 1e-8));
 }
 
-/* ************************************************************************* */
-// Test by Marco Camurri to debug issue #561
-TEST(OrientedPlane3Factor, Issue561) {
-  // Typedefs
-  using symbol_shorthand::P;  //< Planes
-  using symbol_shorthand::X;  //< Pose3 (x,y,z,r,p,y)
-  using Plane = OrientedPlane3;
-
-  NonlinearFactorGraph graph;
-
-  // Setup prior factors
-  Pose3 x0_prior(
-      Rot3(0.799903913, -0.564527097, 0.203624376, 0.552537226, 0.82520195,
-           0.117236322, -0.234214312, 0.0187322547, 0.972004505),
-      Vector3{-91.7500013, -0.47569366, -2.2});
-  auto x0_noise = noiseModel::Isotropic::Sigma(6, 0.01);
-  graph.addPrior<Pose3>(X(0), x0_prior, x0_noise);
-
-//   Plane p1_prior(0.211098835, 0.214292752, 0.95368543, 26.4269514);
-//   auto p1_noise =
-//       noiseModel::Diagonal::Sigmas(Vector3{0.785398163, 0.785398163, 5});
-//   graph.addPrior<Plane>(P(1), p1_prior, p1_noise);
-
-// ADDING THIS PRIOR MAKES OPTIMIZATION FAIL
-//   Plane p2_prior(0.301901811, 0.151751467, 0.941183717, 33.4388229);
-//   auto p2_noise =
-//       noiseModel::Diagonal::Sigmas(Vector3{0.785398163, 0.785398163, 5});
-//   graph.addPrior<Plane>(P(2), p2_prior, p2_noise);
-
-  // First plane factor
-  Plane p1_meas = Plane(0.0638967294, 0.0755284627, 0.995094297, 2.55956073);
-  const auto x0_p1_noise = noiseModel::Isotropic::Sigma(3, 0.0451801);
-  graph.emplace_shared<OrientedPlane3Factor>(p1_meas.planeCoefficients(),
-                                             x0_p1_noise, X(0), P(1));
-
-  // Second plane factor
-  Plane p2_meas = Plane(0.104902077, -0.0275756528, 0.994100165, 1.32765088);
-  const auto x0_p2_noise = noiseModel::Isotropic::Sigma(3, 0.0322889);
-  graph.emplace_shared<OrientedPlane3Factor>(p2_meas.planeCoefficients(),
-                                             x0_p2_noise, X(0), P(2));
-
-  // Optimize
-  try {
-    // Initial values
-    Values initialEstimate;
-    Plane p1(0.211098835, 0.214292752, 0.95368543, 26.4269514);
-    Plane p2(0.301901811, 0.151751467, 0.941183717, 33.4388229);
-    Pose3 x0(
-        Rot3(0.799903913, -0.564527097, 0.203624376, 0.552537226, 0.82520195,
-             0.117236322, -0.234214312, 0.0187322547, 0.972004505),
-        Vector3{-91.7500013, -0.47569366, -2.2});
-    initialEstimate.insert(P(1), p1);
-    initialEstimate.insert(P(2), p2);
-    initialEstimate.insert(X(0), x0);
-
-    GaussNewtonParams params;
-    GTSAM_PRINT(graph);
-    Ordering ordering = list_of(P(1))(P(2))(X(0));  // make sure P1 eliminated first
-    params.setOrdering(ordering);
-    params.setLinearSolverType("SEQUENTIAL_QR");  // abundance of caution
-    params.setVerbosity("TERMINATION");  // show info about stopping conditions
-    GaussNewtonOptimizer optimizer(graph, initialEstimate, params);
-    Values result = optimizer.optimize();
-    EXPECT_DOUBLES_EQUAL(0, graph.error(result), 0.1);
-  } catch (const IndeterminantLinearSystemException &e) {
-    std::cerr << "CAPTURED THE EXCEPTION: " << DefaultKeyFormatter(e.nearbyVariable()) << std::endl;
-    EXPECT(false); // fail if this happens
-  }
-}
-
 /* ************************************************************************* */
 // Simplified version of the test by Marco Camurri to debug issue #561
 TEST(OrientedPlane3Factor, Issue561Simplified) {
   // Typedefs
-  using symbol_shorthand::P;  //< Planes
-  using symbol_shorthand::X;  //< Pose3 (x,y,z,r,p,y)
   using Plane = OrientedPlane3;
 
   NonlinearFactorGraph graph;
 
   // Setup prior factors
-  Pose3 x0_prior(Rot3::identity(), Vector3(99, 0, 0));
+  Pose3 x0(Rot3::identity(), Vector3(0, 0, 10));
   auto x0_noise = noiseModel::Isotropic::Sigma(6, 0.01);
-  graph.addPrior<Pose3>(X(0), x0_prior, x0_noise);
+  graph.addPrior<Pose3>(X(0), x0, x0_noise);
 
   // Two horizontal planes with different heights.
   const Plane p1(0,0,1,1), p2(0,0,1,2);
@@ -300,22 +226,29 @@ TEST(OrientedPlane3Factor, Issue561Simplified) {
   graph.addPrior<Plane>(P(2), p2, p2_noise);
 
   // First plane factor
+  auto p1_measured_from_x0 = p1.transform(x0);
   const auto x0_p1_noise = noiseModel::Isotropic::Sigma(3, 0.05);
-  graph.emplace_shared<OrientedPlane3Factor>(p1.planeCoefficients(),
+  graph.emplace_shared<OrientedPlane3Factor>(
-                                             x0_p1_noise, X(0), P(1));
+      p1_measured_from_x0.planeCoefficients(), x0_p1_noise, X(0), P(1));
 
   // Second plane factor
+  auto p2_measured_from_x0 = p2.transform(x0);
   const auto x0_p2_noise = noiseModel::Isotropic::Sigma(3, 0.05);
-  graph.emplace_shared<OrientedPlane3Factor>(p2.planeCoefficients(),
+  graph.emplace_shared<OrientedPlane3Factor>(
-                                             x0_p2_noise, X(0), P(2));
+      p2_measured_from_x0.planeCoefficients(), x0_p2_noise, X(0), P(2));
+
+  GTSAM_PRINT(graph);
 
   // Initial values
   // Just offset the initial pose by 1m. This is what we are trying to optimize.
   Values initialEstimate;
-  Pose3 x0 = x0_prior.compose(Pose3(Rot3::identity(), Vector3(1,0,0)));
+  Pose3 x0_initial = x0.compose(Pose3(Rot3::identity(), Vector3(1,0,0)));
   initialEstimate.insert(P(1), p1);
   initialEstimate.insert(P(2), p2);
-  initialEstimate.insert(X(0), x0);
+  initialEstimate.insert(X(0), x0_initial);
+
+  // Print Jacobian
+  cout << graph.linearize(initialEstimate)->augmentedJacobian() << endl << endl;
 
   // For testing only
   HessianFactor::shared_ptr hessianFactor = graph.linearizeToHessianFactor(initialEstimate);
@@ -364,7 +297,7 @@ TEST(OrientedPlane3Factor, Issue561Simplified) {
     GaussNewtonOptimizer optimizer(graph, initialEstimate, params);
     Values result = optimizer.optimize();
     EXPECT_DOUBLES_EQUAL(0, graph.error(result), 0.1);
-    EXPECT(x0_prior.equals(result.at<Pose3>(X(0))));
+    EXPECT(x0.equals(result.at<Pose3>(X(0))));
     EXPECT(p1.equals(result.at<Plane>(P(1))));
     EXPECT(p2.equals(result.at<Plane>(P(2))));
   } catch (const IndeterminantLinearSystemException &e) {