testNonlinearFactor.cpp
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1 /* ----------------------------------------------------------------------------
2 
3  * GTSAM Copyright 2010, Georgia Tech Research Corporation,
4  * Atlanta, Georgia 30332-0415
5  * All Rights Reserved
6  * Authors: Frank Dellaert, et al. (see THANKS for the full author list)
7 
8  * See LICENSE for the license information
9 
10  * -------------------------------------------------------------------------- */
11 
20 /*STL/C++*/
21 #include <iostream>
22 
23 #include <CppUnitLite/TestHarness.h>
24 
25 // TODO: DANGEROUS, create shared pointers
26 #define GTSAM_MAGIC_GAUSSIAN 2
27 
28 #include <gtsam/base/Testable.h>
29 #include <gtsam/base/Matrix.h>
30 #include <tests/smallExample.h>
31 #include <tests/simulated2D.h>
32 #include <gtsam/linear/GaussianFactor.h>
33 #include <gtsam/nonlinear/NonlinearFactorGraph.h>
34 #include <gtsam/inference/Symbol.h>
35 
36 using namespace std;
37 using namespace gtsam;
38 using namespace example;
39 
40 // Convenience for named keys
41 using symbol_shorthand::X;
42 using symbol_shorthand::L;
43 
44 typedef std::shared_ptr<NonlinearFactor > shared_nlf;
45 
46 /* ************************************************************************* */
47 TEST( NonlinearFactor, equals )
48 {
49  SharedNoiseModel sigma(noiseModel::Isotropic::Sigma(2,1.0));
50 
51  // create two nonlinear2 factors
52  Point2 z3(0.,-1.);
53  simulated2D::Measurement f0(z3, sigma, X(1),L(1));
54 
55  // measurement between x2 and l1
56  Point2 z4(-1.5, -1.);
57  simulated2D::Measurement f1(z4, sigma, X(2),L(1));
58 
59  CHECK(assert_equal(f0,f0));
60  CHECK(f0.equals(f0));
61  CHECK(!f0.equals(f1));
62  CHECK(!f1.equals(f0));
63 }
64 
65 /* ************************************************************************* */
66 TEST( NonlinearFactor, equals2 )
67 {
68  // create a non linear factor graph
69  NonlinearFactorGraph fg = createNonlinearFactorGraph();
70 
71  // get two factors
72  NonlinearFactorGraph::sharedFactor f0 = fg[0], f1 = fg[1];
73 
74  CHECK(f0->equals(*f0));
75  CHECK(!f0->equals(*f1));
76  CHECK(!f1->equals(*f0));
77 }
78 
79 /* ************************************************************************* */
80 TEST( NonlinearFactor, NonlinearFactor )
81 {
82  // create a non linear factor graph
83  NonlinearFactorGraph fg = createNonlinearFactorGraph();
84 
85  // create a values structure for the non linear factor graph
86  Values cfg = createNoisyValues();
87 
88  // get the factor "f1" from the factor graph
89  NonlinearFactorGraph::sharedFactor factor = fg[0];
90 
91  // calculate the error_vector from the factor "f1"
92  // error_vector = [0.1 0.1]
93  Vector actual_e = std::dynamic_pointer_cast<NoiseModelFactor>(factor)->unwhitenedError(cfg);
94  CHECK(assert_equal(0.1*Vector::Ones(2),actual_e));
95 
96  // error = 0.5 * [1 1] * [1;1] = 1
97  double expected = 1.0;
98 
99  // calculate the error from the factor "f1"
100  double actual = factor->error(cfg);
101  DOUBLES_EQUAL(expected,actual,0.00000001);
102 }
103 
104 /* ************************************************************************* */
105 TEST(NonlinearFactor, Weight) {
106  // create a values structure for the non linear factor graph
107  Values values;
108 
109  // Instantiate a concrete class version of a NoiseModelFactor
110  PriorFactor<Point2> factor1(X(1), Point2(0, 0));
111  values.insert(X(1), Point2(0.1, 0.1));
112 
113  CHECK(assert_equal(1.0, factor1.weight(values)));
114 
115  // Factor with noise model
116  auto noise = noiseModel::Isotropic::Sigma(2, 0.2);
117  PriorFactor<Point2> factor2(X(2), Point2(1, 1), noise);
118  values.insert(X(2), Point2(1.1, 1.1));
119 
120  CHECK(assert_equal(1.0, factor2.weight(values)));
121 
122  Point2 estimate(3, 3), prior(1, 1);
123  double distance = (estimate - prior).norm();
124 
125  auto gaussian = noiseModel::Isotropic::Sigma(2, 0.2);
126 
127  PriorFactor<Point2> factor;
128 
129  // vector to store all the robust models in so we can test iteratively.
130  vector<noiseModel::Robust::shared_ptr> robust_models;
131 
132  // Fair noise model
133  auto fair = noiseModel::Robust::Create(
134  noiseModel::mEstimator::Fair::Create(1.3998), gaussian);
135  robust_models.push_back(fair);
136 
137  // Huber noise model
138  auto huber = noiseModel::Robust::Create(
139  noiseModel::mEstimator::Huber::Create(1.345), gaussian);
140  robust_models.push_back(huber);
141 
142  // Cauchy noise model
143  auto cauchy = noiseModel::Robust::Create(
144  noiseModel::mEstimator::Cauchy::Create(0.1), gaussian);
145  robust_models.push_back(cauchy);
146 
147  // Tukey noise model
148  auto tukey = noiseModel::Robust::Create(
149  noiseModel::mEstimator::Tukey::Create(4.6851), gaussian);
150  robust_models.push_back(tukey);
151 
152  // Welsch noise model
153  auto welsch = noiseModel::Robust::Create(
154  noiseModel::mEstimator::Welsch::Create(2.9846), gaussian);
155  robust_models.push_back(welsch);
156 
157  // Geman-McClure noise model
158  auto gm = noiseModel::Robust::Create(
159  noiseModel::mEstimator::GemanMcClure::Create(1.0), gaussian);
160  robust_models.push_back(gm);
161 
162  // DCS noise model
163  auto dcs = noiseModel::Robust::Create(
164  noiseModel::mEstimator::DCS::Create(1.0), gaussian);
165  robust_models.push_back(dcs);
166 
167  // L2WithDeadZone noise model
168  auto l2 = noiseModel::Robust::Create(
169  noiseModel::mEstimator::L2WithDeadZone::Create(1.0), gaussian);
170  robust_models.push_back(l2);
171 
172  for(auto&& model: robust_models) {
173  factor = PriorFactor<Point2>(X(3), prior, model);
174  values.clear();
175  values.insert(X(3), estimate);
176  CHECK(assert_equal(model->robust()->weight(distance), factor.weight(values)));
177  }
178 }
179 
180 /* ************************************************************************* */
181 TEST( NonlinearFactor, linearize_f1 )
182 {
183  Values c = createNoisyValues();
184 
185  // Grab a non-linear factor
186  NonlinearFactorGraph nfg = createNonlinearFactorGraph();
187  NonlinearFactorGraph::sharedFactor nlf = nfg[0];
188 
189  // We linearize at noisy config from SmallExample
190  GaussianFactor::shared_ptr actual = nlf->linearize(c);
191 
192  GaussianFactorGraph lfg = createGaussianFactorGraph();
193  GaussianFactor::shared_ptr expected = lfg[0];
194 
195  CHECK(assert_equal(*expected,*actual));
196 
197  // The error |A*dx-b| approximates (h(x0+dx)-z) = -error_vector
198  // Hence i.e., b = approximates z-h(x0) = error_vector(x0)
199  //CHECK(assert_equal(nlf->error_vector(c),actual->get_b()));
200 }
201 
202 /* ************************************************************************* */
203 TEST( NonlinearFactor, linearize_f2 )
204 {
205  Values c = createNoisyValues();
206 
207  // Grab a non-linear factor
208  NonlinearFactorGraph nfg = createNonlinearFactorGraph();
209  NonlinearFactorGraph::sharedFactor nlf = nfg[1];
210 
211  // We linearize at noisy config from SmallExample
212  GaussianFactor::shared_ptr actual = nlf->linearize(c);
213 
214  GaussianFactorGraph lfg = createGaussianFactorGraph();
215  GaussianFactor::shared_ptr expected = lfg[1];
216 
217  CHECK(assert_equal(*expected,*actual));
218 }
219 
220 /* ************************************************************************* */
221 TEST( NonlinearFactor, linearize_f3 )
222 {
223  // Grab a non-linear factor
224  NonlinearFactorGraph nfg = createNonlinearFactorGraph();
225  NonlinearFactorGraph::sharedFactor nlf = nfg[2];
226 
227  // We linearize at noisy config from SmallExample
228  Values c = createNoisyValues();
229  GaussianFactor::shared_ptr actual = nlf->linearize(c);
230 
231  GaussianFactorGraph lfg = createGaussianFactorGraph();
232  GaussianFactor::shared_ptr expected = lfg[2];
233 
234  CHECK(assert_equal(*expected,*actual));
235 }
236 
237 /* ************************************************************************* */
238 TEST( NonlinearFactor, linearize_f4 )
239 {
240  // Grab a non-linear factor
241  NonlinearFactorGraph nfg = createNonlinearFactorGraph();
242  NonlinearFactorGraph::sharedFactor nlf = nfg[3];
243 
244  // We linearize at noisy config from SmallExample
245  Values c = createNoisyValues();
246  GaussianFactor::shared_ptr actual = nlf->linearize(c);
247 
248  GaussianFactorGraph lfg = createGaussianFactorGraph();
249  GaussianFactor::shared_ptr expected = lfg[3];
250 
251  CHECK(assert_equal(*expected,*actual));
252 }
253 
254 /* ************************************************************************* */
255 TEST( NonlinearFactor, size )
256 {
257  // create a non linear factor graph
258  NonlinearFactorGraph fg = createNonlinearFactorGraph();
259 
260  // create a values structure for the non linear factor graph
261  Values cfg = createNoisyValues();
262 
263  // get some factors from the graph
264  NonlinearFactorGraph::sharedFactor factor1 = fg[0], factor2 = fg[1],
265  factor3 = fg[2];
266 
267  CHECK(factor1->size() == 1);
268  CHECK(factor2->size() == 2);
269  CHECK(factor3->size() == 2);
270 }
271 
272 /* ************************************************************************* */
273 TEST( NonlinearFactor, linearize_constraint1 )
274 {
275  SharedDiagonal constraint = noiseModel::Constrained::MixedSigmas(Vector2(0.2,0));
276 
277  Point2 mu(1., -1.);
278  NonlinearFactorGraph::sharedFactor f0(new simulated2D::Prior(mu, constraint, X(1)));
279 
280  Values config;
281  config.insert(X(1), Point2(1.0, 2.0));
282  GaussianFactor::shared_ptr actual = f0->linearize(config);
283 
284  // create expected
285  Vector2 b(0., -3.);
286  JacobianFactor expected(X(1), (Matrix(2, 2) << 5.0, 0.0, 0.0, 1.0).finished(), b,
287  noiseModel::Constrained::MixedSigmas(Vector2(1,0)));
288  CHECK(assert_equal((const GaussianFactor&)expected, *actual));
289 }
290 
291 /* ************************************************************************* */
292 TEST( NonlinearFactor, linearize_constraint2 )
293 {
294  SharedDiagonal constraint = noiseModel::Constrained::MixedSigmas(Vector2(0.2,0));
295 
296  Point2 z3(1.,-1.);
297  simulated2D::Measurement f0(z3, constraint, X(1),L(1));
298 
299  Values config;
300  config.insert(X(1), Point2(1.0, 2.0));
301  config.insert(L(1), Point2(5.0, 4.0));
302  GaussianFactor::shared_ptr actual = f0.linearize(config);
303 
304  // create expected
305  Matrix2 A; A << 5.0, 0.0, 0.0, 1.0;
306  Vector2 b(-15., -3.);
307  JacobianFactor expected(X(1), -1*A, L(1), A, b,
308  noiseModel::Constrained::MixedSigmas(Vector2(1,0)));
309  CHECK(assert_equal((const GaussianFactor&)expected, *actual));
310 }
311 
312 /* ************************************************************************* */
313 TEST( NonlinearFactor, cloneWithNewNoiseModel )
314 {
315  // create original factor
316  double sigma1 = 0.1;
317  NonlinearFactorGraph nfg = example::nonlinearFactorGraphWithGivenSigma(sigma1);
318 
319  // create expected
320  double sigma2 = 10;
321  NonlinearFactorGraph expected = example::nonlinearFactorGraphWithGivenSigma(sigma2);
322 
323  // create actual
324  NonlinearFactorGraph actual;
325  SharedNoiseModel noise2 = noiseModel::Isotropic::Sigma(2,sigma2);
326  actual.push_back(nfg.at<NoiseModelFactor>(0)->cloneWithNewNoiseModel(noise2));
327 
328  // check it's all good
329  CHECK(assert_equal(expected, actual));
330 }
331 
332 /* ************************************************************************* */
333 class TestFactor1 : public NoiseModelFactor1<double> {
334  static_assert(std::is_same<Base, NoiseModelFactor>::value, "Base type wrong");
335  static_assert(std::is_same<This, NoiseModelFactor1<double>>::value,
336  "This type wrong");
337 
338  public:
339  typedef NoiseModelFactor1<double> Base;
340 
341  // Provide access to the Matrix& version of evaluateError:
342  using Base::evaluateError;
343 
344  TestFactor1() : Base(noiseModel::Diagonal::Sigmas(Vector1(2.0)), L(1)) {}
345 
346  // Provide access to the Matrix& version of evaluateError:
347  using Base::NoiseModelFactor1; // inherit constructors
348 
349  Vector evaluateError(const double& x1, OptionalMatrixType H1) const override {
350  if (H1) *H1 = (Matrix(1, 1) << 1.0).finished();
351  return (Vector(1) << x1).finished();
352  }
353 
354  gtsam::NonlinearFactor::shared_ptr clone() const override {
355  return std::static_pointer_cast<gtsam::NonlinearFactor>(
356  gtsam::NonlinearFactor::shared_ptr(new TestFactor1(*this)));
357  }
358 };
359 
360 /* ************************************ */
361 TEST(NonlinearFactor, NoiseModelFactor1) {
362  TestFactor1 tf;
363  Values tv;
364  tv.insert(L(1), double((1.0)));
365  EXPECT(assert_equal((Vector(1) << 1.0).finished(), tf.unwhitenedError(tv)));
366  DOUBLES_EQUAL(0.25 / 2.0, tf.error(tv), 1e-9);
367  JacobianFactor jf(
368  *std::dynamic_pointer_cast<JacobianFactor>(tf.linearize(tv)));
369  LONGS_EQUAL((long)L(1), (long)jf.keys()[0]);
370  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 0.5).finished(),
371  jf.getA(jf.begin())));
372  EXPECT(assert_equal((Vector)(Vector(1) << -0.5).finished(), jf.getb()));
373 
374  // Test all functions/types for backwards compatibility
375  static_assert(std::is_same<TestFactor1::X, double>::value,
376  "X type incorrect");
377  EXPECT(assert_equal(tf.key(), L(1)));
378  std::vector<Matrix> H = {Matrix()};
379  EXPECT(assert_equal(Vector1(1.0), tf.unwhitenedError(tv, H)));
380 
381  // Test constructors
382  TestFactor1 tf2(noiseModel::Unit::Create(1), L(1));
383  TestFactor1 tf3(noiseModel::Unit::Create(1), {L(1)});
384  TestFactor1 tf4(noiseModel::Unit::Create(1), gtsam::Symbol('L', 1));
385 }
386 
387 /* ************************************************************************* */
388 class TestFactor4 : public NoiseModelFactor4<double, double, double, double> {
389  static_assert(std::is_same<Base, NoiseModelFactor>::value, "Base type wrong");
390  static_assert(
391  std::is_same<This,
392  NoiseModelFactor4<double, double, double, double>>::value,
393  "This type wrong");
394 
395  public:
396  typedef NoiseModelFactor4<double, double, double, double> Base;
397 
398  // Provide access to the Matrix& version of evaluateError:
399  using Base::evaluateError;
400 
401  TestFactor4() : Base(noiseModel::Diagonal::Sigmas((Vector(1) << 2.0).finished()), X(1), X(2), X(3), X(4)) {}
402 
403  // Provide access to the Matrix& version of evaluateError:
404  using Base::NoiseModelFactor4; // inherit constructors
405 
406  Vector
407  evaluateError(const double& x1, const double& x2, const double& x3, const double& x4,
408  OptionalMatrixType H1, OptionalMatrixType H2,
409  OptionalMatrixType H3, OptionalMatrixType H4) const override {
410  if(H1) {
411  *H1 = (Matrix(1, 1) << 1.0).finished();
412  *H2 = (Matrix(1, 1) << 2.0).finished();
413  *H3 = (Matrix(1, 1) << 3.0).finished();
414  *H4 = (Matrix(1, 1) << 4.0).finished();
415  }
416  return (Vector(1) << x1 + 2.0 * x2 + 3.0 * x3 + 4.0 * x4).finished();
417  }
418 
419  gtsam::NonlinearFactor::shared_ptr clone() const override {
420  return std::static_pointer_cast<gtsam::NonlinearFactor>(
421  gtsam::NonlinearFactor::shared_ptr(new TestFactor4(*this))); }
422 };
423 
424 /* ************************************ */
425 TEST(NonlinearFactor, NoiseModelFactor4) {
426  TestFactor4 tf;
427  Values tv;
428  tv.insert(X(1), double((1.0)));
429  tv.insert(X(2), double((2.0)));
430  tv.insert(X(3), double((3.0)));
431  tv.insert(X(4), double((4.0)));
432  EXPECT(assert_equal((Vector(1) << 30.0).finished(), tf.unwhitenedError(tv)));
433  DOUBLES_EQUAL(0.5 * 30.0 * 30.0 / 4.0, tf.error(tv), 1e-9);
434  JacobianFactor jf(*std::dynamic_pointer_cast<JacobianFactor>(tf.linearize(tv)));
435  LONGS_EQUAL((long)X(1), (long)jf.keys()[0]);
436  LONGS_EQUAL((long)X(2), (long)jf.keys()[1]);
437  LONGS_EQUAL((long)X(3), (long)jf.keys()[2]);
438  LONGS_EQUAL((long)X(4), (long)jf.keys()[3]);
439  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 0.5).finished(), jf.getA(jf.begin())));
440  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.0).finished(), jf.getA(jf.begin()+1)));
441  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.5).finished(), jf.getA(jf.begin()+2)));
442  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 2.0).finished(), jf.getA(jf.begin()+3)));
443  EXPECT(assert_equal((Vector)(Vector(1) << 0.5 * -30.).finished(), jf.getb()));
444 
445  // Test all functions/types for backwards compatibility
446  static_assert(std::is_same<TestFactor4::X1, double>::value,
447  "X1 type incorrect");
448  static_assert(std::is_same<TestFactor4::X2, double>::value,
449  "X2 type incorrect");
450  static_assert(std::is_same<TestFactor4::X3, double>::value,
451  "X3 type incorrect");
452  static_assert(std::is_same<TestFactor4::X4, double>::value,
453  "X4 type incorrect");
454  EXPECT(assert_equal(tf.key1(), X(1)));
455  EXPECT(assert_equal(tf.key2(), X(2)));
456  EXPECT(assert_equal(tf.key3(), X(3)));
457  EXPECT(assert_equal(tf.key4(), X(4)));
458  std::vector<Matrix> H = {Matrix(), Matrix(), Matrix(), Matrix()};
459  EXPECT(assert_equal(Vector1(30.0), tf.unwhitenedError(tv, H)));
460  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.).finished(), H.at(0)));
461  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 2.).finished(), H.at(1)));
462  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 3.).finished(), H.at(2)));
463  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 4.).finished(), H.at(3)));
464 
465  // And test "forward compatibility" using `key<N>` and `ValueType<N>` too
466  static_assert(std::is_same<TestFactor4::ValueType<1>, double>::value,
467  "ValueType<1> type incorrect");
468  static_assert(std::is_same<TestFactor4::ValueType<2>, double>::value,
469  "ValueType<2> type incorrect");
470  static_assert(std::is_same<TestFactor4::ValueType<3>, double>::value,
471  "ValueType<3> type incorrect");
472  static_assert(std::is_same<TestFactor4::ValueType<4>, double>::value,
473  "ValueType<4> type incorrect");
474  EXPECT(assert_equal(tf.key<1>(), X(1)));
475  EXPECT(assert_equal(tf.key<2>(), X(2)));
476  EXPECT(assert_equal(tf.key<3>(), X(3)));
477  EXPECT(assert_equal(tf.key<4>(), X(4)));
478 
479  // Test constructors
480  TestFactor4 tf2(noiseModel::Unit::Create(1), L(1), L(2), L(3), L(4));
481  TestFactor4 tf3(noiseModel::Unit::Create(1), {L(1), L(2), L(3), L(4)});
482  TestFactor4 tf4(noiseModel::Unit::Create(1),
483  std::array<Key, 4>{L(1), L(2), L(3), L(4)});
484  std::vector<Key> keys = {L(1), L(2), L(3), L(4)};
485  TestFactor4 tf5(noiseModel::Unit::Create(1), keys);
486 }
487 
488 /* ************************************************************************* */
489 class TestFactor5 : public NoiseModelFactor5<double, double, double, double, double> {
490 public:
491  typedef NoiseModelFactor5<double, double, double, double, double> Base;
492 
493  // Provide access to the Matrix& version of evaluateError:
494  using Base::evaluateError;
495 
496  TestFactor5() : Base(noiseModel::Diagonal::Sigmas((Vector(1) << 2.0).finished()), X(1), X(2), X(3), X(4), X(5)) {}
497 
498  Vector
499  evaluateError(const X1& x1, const X2& x2, const X3& x3, const X4& x4, const X5& x5,
500  OptionalMatrixType H1, OptionalMatrixType H2, OptionalMatrixType H3,
501  OptionalMatrixType H4, OptionalMatrixType H5) const override {
502  if(H1) {
503  *H1 = (Matrix(1, 1) << 1.0).finished();
504  *H2 = (Matrix(1, 1) << 2.0).finished();
505  *H3 = (Matrix(1, 1) << 3.0).finished();
506  *H4 = (Matrix(1, 1) << 4.0).finished();
507  *H5 = (Matrix(1, 1) << 5.0).finished();
508  }
509  return (Vector(1) << x1 + 2.0 * x2 + 3.0 * x3 + 4.0 * x4 + 5.0 * x5)
510  .finished();
511  }
512 };
513 
514 /* ************************************ */
515 TEST(NonlinearFactor, NoiseModelFactor5) {
516  TestFactor5 tf;
517  Values tv;
518  tv.insert(X(1), double((1.0)));
519  tv.insert(X(2), double((2.0)));
520  tv.insert(X(3), double((3.0)));
521  tv.insert(X(4), double((4.0)));
522  tv.insert(X(5), double((5.0)));
523  EXPECT(assert_equal((Vector(1) << 55.0).finished(), tf.unwhitenedError(tv)));
524  DOUBLES_EQUAL(0.5 * 55.0 * 55.0 / 4.0, tf.error(tv), 1e-9);
525  JacobianFactor jf(*std::dynamic_pointer_cast<JacobianFactor>(tf.linearize(tv)));
526  LONGS_EQUAL((long)X(1), (long)jf.keys()[0]);
527  LONGS_EQUAL((long)X(2), (long)jf.keys()[1]);
528  LONGS_EQUAL((long)X(3), (long)jf.keys()[2]);
529  LONGS_EQUAL((long)X(4), (long)jf.keys()[3]);
530  LONGS_EQUAL((long)X(5), (long)jf.keys()[4]);
531  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 0.5).finished(), jf.getA(jf.begin())));
532  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.0).finished(), jf.getA(jf.begin()+1)));
533  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.5).finished(), jf.getA(jf.begin()+2)));
534  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 2.0).finished(), jf.getA(jf.begin()+3)));
535  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 2.5).finished(), jf.getA(jf.begin()+4)));
536  EXPECT(assert_equal((Vector)(Vector(1) << 0.5 * -55.).finished(), jf.getb()));
537 }
538 
539 /* ************************************************************************* */
540 class TestFactor6 : public NoiseModelFactor6<double, double, double, double, double, double> {
541 public:
542  typedef NoiseModelFactor6<double, double, double, double, double, double> Base;
543 
544  // Provide access to the Matrix& version of evaluateError:
545  using Base::evaluateError;
546 
547  TestFactor6() : Base(noiseModel::Diagonal::Sigmas((Vector(1) << 2.0).finished()), X(1), X(2), X(3), X(4), X(5), X(6)) {}
548 
549  Vector
550  evaluateError(const X1& x1, const X2& x2, const X3& x3, const X4& x4, const X5& x5, const X6& x6,
551  OptionalMatrixType H1, OptionalMatrixType H2, OptionalMatrixType H3, OptionalMatrixType H4,
552  OptionalMatrixType H5, OptionalMatrixType H6) const override {
553  if(H1) {
554  *H1 = (Matrix(1, 1) << 1.0).finished();
555  *H2 = (Matrix(1, 1) << 2.0).finished();
556  *H3 = (Matrix(1, 1) << 3.0).finished();
557  *H4 = (Matrix(1, 1) << 4.0).finished();
558  *H5 = (Matrix(1, 1) << 5.0).finished();
559  *H6 = (Matrix(1, 1) << 6.0).finished();
560  }
561  return (Vector(1) << x1 + 2.0 * x2 + 3.0 * x3 + 4.0 * x4 + 5.0 * x5 +
562  6.0 * x6)
563  .finished();
564  }
565 
566 };
567 
568 /* ************************************ */
569 TEST(NonlinearFactor, NoiseModelFactor6) {
570  TestFactor6 tf;
571  Values tv;
572  tv.insert(X(1), double((1.0)));
573  tv.insert(X(2), double((2.0)));
574  tv.insert(X(3), double((3.0)));
575  tv.insert(X(4), double((4.0)));
576  tv.insert(X(5), double((5.0)));
577  tv.insert(X(6), double((6.0)));
578  EXPECT(assert_equal((Vector(1) << 91.0).finished(), tf.unwhitenedError(tv)));
579  DOUBLES_EQUAL(0.5 * 91.0 * 91.0 / 4.0, tf.error(tv), 1e-9);
580  JacobianFactor jf(*std::dynamic_pointer_cast<JacobianFactor>(tf.linearize(tv)));
581  LONGS_EQUAL((long)X(1), (long)jf.keys()[0]);
582  LONGS_EQUAL((long)X(2), (long)jf.keys()[1]);
583  LONGS_EQUAL((long)X(3), (long)jf.keys()[2]);
584  LONGS_EQUAL((long)X(4), (long)jf.keys()[3]);
585  LONGS_EQUAL((long)X(5), (long)jf.keys()[4]);
586  LONGS_EQUAL((long)X(6), (long)jf.keys()[5]);
587  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 0.5).finished(), jf.getA(jf.begin())));
588  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.0).finished(), jf.getA(jf.begin()+1)));
589  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.5).finished(), jf.getA(jf.begin()+2)));
590  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 2.0).finished(), jf.getA(jf.begin()+3)));
591  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 2.5).finished(), jf.getA(jf.begin()+4)));
592  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 3.0).finished(), jf.getA(jf.begin()+5)));
593  EXPECT(assert_equal((Vector)(Vector(1) << 0.5 * -91.).finished(), jf.getb()));
594 
595 }
596 
597 /* ************************************************************************* */
598 class TestFactorN : public NoiseModelFactorN<double, double, double, double> {
599 public:
600  typedef NoiseModelFactorN<double, double, double, double> Base;
601 
602  // Provide access to the Matrix& version of evaluateError:
603  using Base::evaluateError;
604 
605  using Type1 = ValueType<1>; // Test that we can use the ValueType<> template
606 
607  TestFactorN() : Base(noiseModel::Diagonal::Sigmas((Vector(1) << 2.0).finished()), X(1), X(2), X(3), X(4)) {}
608 
609  Vector
610  evaluateError(const double& x1, const double& x2, const double& x3, const double& x4,
611  OptionalMatrixType H1, OptionalMatrixType H2,
612  OptionalMatrixType H3, OptionalMatrixType H4) const override {
613  if (H1) *H1 = (Matrix(1, 1) << 1.0).finished();
614  if (H2) *H2 = (Matrix(1, 1) << 2.0).finished();
615  if (H3) *H3 = (Matrix(1, 1) << 3.0).finished();
616  if (H4) *H4 = (Matrix(1, 1) << 4.0).finished();
617  return (Vector(1) << x1 + 2.0 * x2 + 3.0 * x3 + 4.0 * x4).finished();
618  }
619 
620  Key key1() const { return key<1>(); } // Test that we can use key<> template
621 };
622 
623 /* ************************************ */
624 TEST(NonlinearFactor, NoiseModelFactorN) {
625  TestFactorN tf;
626  Values tv;
627  tv.insert(X(1), double((1.0)));
628  tv.insert(X(2), double((2.0)));
629  tv.insert(X(3), double((3.0)));
630  tv.insert(X(4), double((4.0)));
631  EXPECT(assert_equal((Vector(1) << 30.0).finished(), tf.unwhitenedError(tv)));
632  DOUBLES_EQUAL(0.5 * 30.0 * 30.0 / 4.0, tf.error(tv), 1e-9);
633  JacobianFactor jf(*std::dynamic_pointer_cast<JacobianFactor>(tf.linearize(tv)));
634  LONGS_EQUAL((long)X(1), (long)jf.keys()[0]);
635  LONGS_EQUAL((long)X(2), (long)jf.keys()[1]);
636  LONGS_EQUAL((long)X(3), (long)jf.keys()[2]);
637  LONGS_EQUAL((long)X(4), (long)jf.keys()[3]);
638  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 0.5).finished(), jf.getA(jf.begin())));
639  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.0).finished(), jf.getA(jf.begin()+1)));
640  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 1.5).finished(), jf.getA(jf.begin()+2)));
641  EXPECT(assert_equal((Matrix)(Matrix(1, 1) << 2.0).finished(), jf.getA(jf.begin()+3)));
642  EXPECT(assert_equal((Vector)(Vector(1) << -0.5 * 30.).finished(), jf.getb()));
643 
644  // Test all evaluateError argument overloads to ensure backward compatibility
645  Matrix H1_expected, H2_expected, H3_expected, H4_expected;
646  Vector e_expected = tf.evaluateError(9, 8, 7, 6, H1_expected, H2_expected,
647  H3_expected, H4_expected);
648 
649  std::unique_ptr<NoiseModelFactorN<double, double, double, double>> base_ptr(
650  new TestFactorN(tf));
651  Matrix H1, H2, H3, H4;
652  EXPECT(assert_equal(e_expected, base_ptr->evaluateError(9, 8, 7, 6)));
653  EXPECT(assert_equal(e_expected, base_ptr->evaluateError(9, 8, 7, 6, H1)));
654  EXPECT(assert_equal(H1_expected, H1));
655  EXPECT(assert_equal(e_expected, //
656  base_ptr->evaluateError(9, 8, 7, 6, H1, H2)));
657  EXPECT(assert_equal(H1_expected, H1));
658  EXPECT(assert_equal(H2_expected, H2));
659  EXPECT(assert_equal(e_expected,
660  base_ptr->evaluateError(9, 8, 7, 6, H1, H2, H3)));
661  EXPECT(assert_equal(H1_expected, H1));
662  EXPECT(assert_equal(H2_expected, H2));
663  EXPECT(assert_equal(H3_expected, H3));
664  EXPECT(assert_equal(e_expected,
665  base_ptr->evaluateError(9, 8, 7, 6, H1, H2, H3, H4)));
666  EXPECT(assert_equal(H1_expected, H1));
667  EXPECT(assert_equal(H2_expected, H2));
668  EXPECT(assert_equal(H3_expected, H3));
669  EXPECT(assert_equal(H4_expected, H4));
670 
671  // Test all functions/types for backwards compatibility
672 
673  static_assert(std::is_same<TestFactor4::X1, double>::value,
674  "X1 type incorrect");
675  EXPECT(assert_equal(tf.key3(), X(3)));
676 
677 
678  // Test using `key<N>` and `ValueType<N>`
679  static_assert(std::is_same<TestFactorN::ValueType<1>, double>::value,
680  "ValueType<1> type incorrect");
681  static_assert(std::is_same<TestFactorN::ValueType<2>, double>::value,
682  "ValueType<2> type incorrect");
683  static_assert(std::is_same<TestFactorN::ValueType<3>, double>::value,
684  "ValueType<3> type incorrect");
685  static_assert(std::is_same<TestFactorN::ValueType<4>, double>::value,
686  "ValueType<4> type incorrect");
687  static_assert(std::is_same<TestFactorN::Type1, double>::value,
688  "TestFactorN::Type1 type incorrect");
689  EXPECT(assert_equal(tf.key<1>(), X(1)));
690  EXPECT(assert_equal(tf.key<2>(), X(2)));
691  EXPECT(assert_equal(tf.key<3>(), X(3)));
692  EXPECT(assert_equal(tf.key<4>(), X(4)));
693  EXPECT(assert_equal(tf.key1(), X(1)));
694 }
695 
696 /* ************************************************************************* */
697 TEST( NonlinearFactor, clone_rekey )
698 {
699  shared_nlf init(new TestFactor4());
700  EXPECT_LONGS_EQUAL((long)X(1), (long)init->keys()[0]);
701  EXPECT_LONGS_EQUAL((long)X(2), (long)init->keys()[1]);
702  EXPECT_LONGS_EQUAL((long)X(3), (long)init->keys()[2]);
703  EXPECT_LONGS_EQUAL((long)X(4), (long)init->keys()[3]);
704 
705  // Standard clone
706  shared_nlf actClone = init->clone();
707  EXPECT(actClone.get() != init.get()); // Ensure different pointers
708  EXPECT(assert_equal(*init, *actClone));
709 
710  // Re-key factor - clones with different keys
711  KeyVector new_keys {X(5),X(6),X(7),X(8)};
712  shared_nlf actRekey = init->rekey(new_keys);
713  EXPECT(actRekey.get() != init.get()); // Ensure different pointers
714 
715  // Ensure init is unchanged
716  EXPECT_LONGS_EQUAL((long)X(1), (long)init->keys()[0]);
717  EXPECT_LONGS_EQUAL((long)X(2), (long)init->keys()[1]);
718  EXPECT_LONGS_EQUAL((long)X(3), (long)init->keys()[2]);
719  EXPECT_LONGS_EQUAL((long)X(4), (long)init->keys()[3]);
720 
721  // Check new keys
722  EXPECT_LONGS_EQUAL((long)X(5), (long)actRekey->keys()[0]);
723  EXPECT_LONGS_EQUAL((long)X(6), (long)actRekey->keys()[1]);
724  EXPECT_LONGS_EQUAL((long)X(7), (long)actRekey->keys()[2]);
725  EXPECT_LONGS_EQUAL((long)X(8), (long)actRekey->keys()[3]);
726 }
727 
728 /* ************************************************************************* */
729 int main() { TestResult tr; return TestRegistry::runAllTests(tr);}
730 /* ************************************************************************* */
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