GCC Code Coverage Report

Directory: ./
File: tests/features/test_feature_generic.cpp
Date: 2024-12-13 12:22:33
Exec Total Coverage
Lines: 349 358 97.5%
Branches: 732 1443 50.7%
Line Branch Exec Source
1 /*
2 * Copyright 2019,
3 * François Bleibel,
4 * Olivier Stasse,
5 *
6 * CNRS/AIST
7 *
8 */
9
10 /* -------------------------------------------------------------------------- */
11 /* --- INCLUDES ------------------------------------------------------------- */
12 /* -------------------------------------------------------------------------- */
13 #include <iostream>
14 #include <pinocchio/algorithm/frames.hpp>
15 #include <pinocchio/algorithm/jacobian.hpp>
16 #include <pinocchio/algorithm/joint-configuration.hpp>
17 #include <pinocchio/algorithm/kinematics.hpp>
18 #include <pinocchio/multibody/data.hpp>
19 #include <pinocchio/multibody/liegroup/liegroup.hpp>
20 #include <pinocchio/multibody/model.hpp>
21 #include <pinocchio/parsers/sample-models.hpp>
22
23 #define BOOST_TEST_MODULE features
24 #include <dynamic-graph/factory.h>
25 #include <dynamic-graph/linear-algebra.h>
26
27 #include <Eigen/SVD>
28 #include <boost/test/unit_test.hpp>
29 #include <sot/core/debug.hh>
30 #include <sot/core/feature-abstract.hh>
31 #include <sot/core/feature-generic.hh>
32 #include <sot/core/feature-pose.hh>
33 #include <sot/core/gain-adaptive.hh>
34 #include <sot/core/sot.hh>
35 #include <sot/core/task.hh>
36
37 namespace dynamicgraph {
38 namespace sot {
39 namespace dg = dynamicgraph;
40
41 typedef pinocchio::CartesianProductOperation<
42 pinocchio::VectorSpaceOperationTpl<3, double>,
43 pinocchio::SpecialOrthogonalOperationTpl<3, double> >
44 R3xSO3_t;
45 typedef pinocchio::SpecialEuclideanOperationTpl<3, double> SE3_t;
46
47 namespace internal {
48 template <Representation_t representation>
49 struct LG_t {
50 typedef typename boost::mpl::if_c<representation == SE3Representation, SE3_t,
51 R3xSO3_t>::type type;
52 };
53 } // namespace internal
54 } // namespace sot
55 } // namespace dynamicgraph
56
57 using namespace std;
58 using namespace dynamicgraph::sot;
59 using namespace dynamicgraph;
60
61 #define EIGEN_VECTOR_IS_APPROX(Va, Vb, precision) \
62 BOOST_CHECK_MESSAGE((Va).isApprox(Vb, precision), \
63 "check " #Va ".isApprox(" #Vb \
64 ") failed " \
65 "[\n" \
66 << (Va).transpose() << "\n!=\n" \
67 << (Vb).transpose() << "\n]")
68 #define EIGEN_MATRIX_IS_APPROX(Va, Vb, precision) \
69 BOOST_CHECK_MESSAGE((Va).isApprox(Vb, precision), "check " #Va \
70 ".isApprox(" #Vb \
71 ") failed " \
72 "[\n" \
73 << (Va) << "\n!=\n" \
74 << (Vb) << "\n]")
75
76 class FeatureTestBase {
77 public:
78 Task task_;
79 int time_;
80 dynamicgraph::Vector expectedTaskOutput_;
81
82 5 FeatureTestBase(unsigned dim, const std::string &name)
83
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 5 : task_("task" + name), time_(0) {
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 5 expectedTaskOutput_.resize(dim);
85 5 }
86
87 5 virtual void init() {
88 5 task_.addFeature(featureAbstract());
89
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 5 task_.setWithDerivative(true);
90 5 }
91
92 26 void computeExpectedTaskOutput(const Vector &error,
93 const Vector &errorDrift) {
94 26 double gain = task_.controlGainSIN;
95
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 26 expectedTaskOutput_ = -gain * error - errorDrift;
96 26 }
97
98 template <typename LG_t>
99 32 void computeExpectedTaskOutput(const Vector &s, const Vector &sdes,
100 const Vector &sDesDot, const LG_t &lg) {
101
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 32 Vector s_sd(lg.nv());
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 32 lg.difference(sdes, s, s_sd);
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 32 Eigen::Matrix<typename LG_t::Scalar, LG_t::NV, LG_t::NV> Jminus(lg.nv(),
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 32 lg.nv());
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 32 lg.template dDifference<pinocchio::ARG0>(sdes, s, Jminus);
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108
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 32 computeExpectedTaskOutput(s_sd, Jminus * sDesDot);
109 32 }
110
111 26 void checkTaskOutput() {
112
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 26 BOOST_REQUIRE_EQUAL(task_.taskSOUT.accessCopy().size(),
113 expectedTaskOutput_.size());
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 26 Vector taskOutput(expectedTaskOutput_.size());
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 182 for (int i = 0; i < expectedTaskOutput_.size(); ++i)
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 156 taskOutput[i] = task_.taskSOUT.accessCopy()[i].getSingleBound();
117
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 26 EIGEN_VECTOR_IS_APPROX(taskOutput, expectedTaskOutput_, 1e-6);
118 26 }
119
120 58 void setGain(double gain) {
121 58 task_.controlGainSIN = gain;
122 58 task_.controlGainSIN.access(time_);
123 58 task_.controlGainSIN.setReady();
124 58 }
125
126 template <typename SignalType, typename ValueType>
127 2280 void setSignal(SignalType &sig, const ValueType &v) {
128
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 2280 sig = v;
129 2280 sig.access(time_);
130 2280 sig.setReady();
131 2280 }
132
133 virtual FeatureAbstract &featureAbstract() = 0;
134
135 virtual void setInputs() = 0;
136
137 ✗ virtual void printInputs() {}
138
139 26 virtual void recompute() {
140 26 task_.taskSOUT.recompute(time_);
141
142 // Check that recomputing went fine.
143 26 FeatureAbstract &f(featureAbstract());
144
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 26 BOOST_CHECK_EQUAL(time_, f.errorSOUT.getTime());
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 26 BOOST_CHECK_EQUAL(time_, f.errordotSOUT.getTime());
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 26 BOOST_CHECK_EQUAL(time_, task_.errorSOUT.getTime());
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 26 BOOST_CHECK_EQUAL(time_, task_.errorTimeDerivativeSOUT.getTime());
148
149 // TODO check that the output is correct
150 // computeExpectedTaskOutput (s - sd, - vd);
151 26 }
152
153 ✗ virtual void printOutputs() {}
154
155 26 virtual void checkValue() {
156 26 time_++;
157 26 setInputs();
158 26 printInputs();
159 26 recompute();
160 26 printOutputs();
161 26 }
162 };
163
164 class TestFeatureGeneric : public FeatureTestBase {
165 public:
166 FeatureGeneric feature_, featureDes_;
167 int dim_;
168
169 1 TestFeatureGeneric(unsigned dim, const std::string &name)
170 1 : FeatureTestBase(dim, name),
171
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 1 feature_("feature" + name),
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 1 featureDes_("featureDes" + name),
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 1 dim_(dim) {
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 1 feature_.setReference(&featureDes_);
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 1 feature_.selectionSIN = Flags(true);
176
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 1 dynamicgraph::Matrix Jq(dim, dim);
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 1 Jq.setIdentity();
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 1 feature_.jacobianSIN.setReference(&Jq);
180
181
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 1 init();
182 1 }
183
184 11 FeatureAbstract &featureAbstract() { return feature_; }
185
186 10 void setInputs() {
187
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 10 dynamicgraph::Vector s(dim_), sd(dim_), vd(dim_);
188 double gain;
189
190
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 10 switch (time_) {
191 ✗ case 0:
192 ✗ BOOST_TEST_MESSAGE(" ----- Test Velocity -----");
193 ✗ s.setZero();
194 ✗ sd.setZero();
195 ✗ vd.setZero();
196 ✗ gain = 0.0;
197 ✗ break;
198 1 case 1:
199
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 1 BOOST_TEST_MESSAGE(" ----- Test Position -----");
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 1 s.setZero();
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 1 sd.setConstant(2.);
202
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 1 vd.setZero();
203 1 gain = 1.0;
204 1 break;
205 1 case 2:
206
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 1 BOOST_TEST_MESSAGE(" ----- Test both -----");
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 1 s.setZero();
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 1 sd.setConstant(2.);
209
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 1 vd.setConstant(1.);
210 1 gain = 3.0;
211 1 break;
212 8 default:
213
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 8 s.setRandom();
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 8 sd.setRandom();
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 8 vd.setRandom();
216 8 gain = 1.0;
217 }
218
219
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 10 feature_.errorSIN = s;
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 10 feature_.errorSIN.access(time_);
221 10 feature_.errorSIN.setReady();
222
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 10 featureDes_.errorSIN = sd;
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 10 featureDes_.errorSIN.access(time_);
225 10 featureDes_.errorSIN.setReady();
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227
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 10 featureDes_.errordotSIN = vd;
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 10 featureDes_.errordotSIN.access(time_);
229 10 featureDes_.errordotSIN.setReady();
230
231
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 10 setGain(gain);
232 10 }
233
234 10 void printInputs() {
235
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 10 BOOST_TEST_MESSAGE("----- inputs -----");
236
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 10 BOOST_TEST_MESSAGE(
237 "feature_.errorSIN: " << feature_.errorSIN(time_).transpose());
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 10 BOOST_TEST_MESSAGE(
239 "featureDes_.errorSIN: " << featureDes_.errorSIN(time_).transpose());
240
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 10 BOOST_TEST_MESSAGE("featureDes_.errordotSIN: "
241 << featureDes_.errordotSIN(time_).transpose());
242
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 10 BOOST_TEST_MESSAGE("task.controlGain: " << task_.controlGainSIN(time_));
243 10 }
244
245 10 void recompute() {
246
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 10 FeatureTestBase::recompute();
247
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 10 dynamicgraph::Vector s = feature_.errorSIN;
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 10 dynamicgraph::Vector sd = featureDes_.errorSIN;
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 10 dynamicgraph::Vector vd = featureDes_.errordotSIN;
251
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 10 computeExpectedTaskOutput(s - sd, -vd);
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 10 checkTaskOutput();
255 10 }
256
257 10 void printOutputs() {
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 10 BOOST_TEST_MESSAGE("----- output -----");
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 10 BOOST_TEST_MESSAGE("time: " << time_);
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 10 BOOST_TEST_MESSAGE(
261 "feature.errorSOUT: " << feature_.errorSOUT(time_).transpose());
262
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 10 BOOST_TEST_MESSAGE(
263 "feature.errordotSOUT: " << feature_.errordotSOUT(time_).transpose());
264
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 10 BOOST_TEST_MESSAGE("task.taskSOUT: " << task_.taskSOUT(time_));
265 // BOOST_TEST_MESSAGE("task.errorSOUT: " << task_.errorSOUT(time_));
266 // BOOST_TEST_MESSAGE("task.errordtSOUT: " <<
267 // task_.errorTimeDerivativeSOUT(time_));
268
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 10 BOOST_TEST_MESSAGE(
269 "Expected task output: " << expectedTaskOutput_.transpose());
270 10 }
271 };
272
273 BOOST_AUTO_TEST_SUITE(feature_generic)
274
275
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 4 BOOST_AUTO_TEST_CASE(check_value) {
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 2 std::string srobot("test");
277 2 unsigned int dim = 6;
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 2 TestFeatureGeneric testFeatureGeneric(dim, srobot);
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281
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 22 for (int i = 0; i < 10; ++i) testFeatureGeneric.checkValue();
282 2 }
283
284 BOOST_AUTO_TEST_SUITE_END() // feature_generic
285
286 48 MatrixHomogeneous randomM() {
287
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 48 MatrixHomogeneous M;
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 48 M.translation().setRandom();
289
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 48 Eigen::Vector3d w(Eigen::Vector3d::Random());
290
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 48 M.linear() = Eigen::AngleAxisd(w.norm(), w.normalized()).matrix();
291 96 return M;
292 }
293
294 typedef pinocchio::SE3 SE3;
295
296 32 Vector7 toVector(const pinocchio::SE3 &M) {
297 32 Vector7 v;
298
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299
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 32 QuaternionMap(v.tail<4>().data()) = M.rotation();
300 32 return v;
301 }
302
303 96 Vector toVector(const std::vector<MultiBound> &in) {
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 96 Vector out(in.size());
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 672 for (int i = 0; i < (int)in.size(); ++i) out[i] = in[i].getSingleBound();
306 96 return out;
307 }
308
309 template <Representation_t representation>
310 class TestFeaturePose : public FeatureTestBase {
311 public:
312 typedef typename dg::sot::internal::LG_t<representation>::type LieGroup_t;
313 FeaturePose<representation> feature_;
314 bool relative_;
315 pinocchio::Model model_;
316 pinocchio::Data data_;
317 pinocchio::JointIndex ja_, jb_;
318 pinocchio::FrameIndex fa_, fb_;
319
320 8 TestFeaturePose(bool relative, const std::string &name)
321 : FeatureTestBase(6, name),
322
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 8 feature_("feature" + name),
323 8 relative_(relative),
324
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 16 data_(model_) {
325
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 8 pinocchio::buildModels::humanoid(model_, true); // use freeflyer
326
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 8 model_.lowerPositionLimit.head<3>().setConstant(-1.);
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328
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 8 jb_ = model_.getJointId("rarm_wrist2_joint");
329
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 16 fb_ = model_.addFrame(pinocchio::Model::Frame(
330 "frame_b", jb_,
331 8 model_.getFrameId("rarm_wrist2_joint", pinocchio::JOINT),
332 SE3::Identity(), pinocchio::OP_FRAME));
333
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 8 if (relative) {
334
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 4 ja_ = model_.getJointId("larm_wrist2_joint");
335
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 8 fa_ = model_.addFrame(pinocchio::Model::Frame(
336 "frame_a", ja_,
337 8 model_.getFrameId("larm_wrist2_joint", pinocchio::JOINT),
338 SE3::Identity(), pinocchio::OP_FRAME));
339 } else {
340 4 ja_ = 0;
341
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 4 fa_ = model_.addFrame(pinocchio::Model::Frame(
342 "frame_a", 0, 0, SE3::Identity(), pinocchio::OP_FRAME));
343 }
344
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 8 data_ = pinocchio::Data(model_);
345
346
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 8 init();
347
348
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 8 setJointFrame();
349 8 }
350
351 20 void _setFrame() {
352 40 setSignal(
353
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 20 feature_.jaMfa,
354
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 20 MatrixHomogeneous(model_.frames[fa_].placement.toHomogeneousMatrix()));
355 40 setSignal(
356
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 20 feature_.jbMfb,
357
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 20 MatrixHomogeneous(model_.frames[fb_].placement.toHomogeneousMatrix()));
358 20 }
359
360 12 void setJointFrame() {
361 12 model_.frames[fa_].placement.setIdentity();
362 12 model_.frames[fb_].placement.setIdentity();
363 12 _setFrame();
364 12 }
365
366 8 void setRandomFrame() {
367 8 model_.frames[fa_].placement.setRandom();
368 8 model_.frames[fb_].placement.setRandom();
369 8 _setFrame();
370 8 }
371
372 40 FeatureAbstract &featureAbstract() { return feature_; }
373
374 32 void setInputs() {
375
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 32 Vector q(pinocchio::randomConfiguration(model_));
376
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 32 pinocchio::framesForwardKinematics(model_, data_, q);
377
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 32 pinocchio::computeJointJacobians(model_, data_);
378
379 // Poses
380
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 32 setSignal(feature_.oMjb,
381
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 32 MatrixHomogeneous(data_.oMi[jb_].toHomogeneousMatrix()));
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 32 if (relative_) {
383
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 16 setSignal(feature_.oMja,
384
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 32 MatrixHomogeneous(data_.oMi[ja_].toHomogeneousMatrix()));
385 }
386
387 // Jacobians
388
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 32 Matrix J(6, model_.nv);
389
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 32 J.setZero();
390
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 32 pinocchio::getJointJacobian(model_, data_, jb_, pinocchio::LOCAL, J);
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 32 setSignal(feature_.jbJjb, J);
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 32 if (relative_) {
393
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 16 J.setZero();
394
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 16 pinocchio::getJointJacobian(model_, data_, ja_, pinocchio::LOCAL, J);
395
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 16 setSignal(feature_.jaJja, J);
396 }
397
398 // Desired
399
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 32 setSignal(feature_.faMfbDes, randomM());
400
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 32 setSignal(feature_.faNufafbDes, Vector::Random(6));
401
402 32 double gain = 0;
403 // if (time_ % 5 != 0)
404 // gain = 2 * (double)rand() / RAND_MAX;
405
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 32 if (time_ % 2 != 0) gain = 1;
406
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 32 setGain(gain);
407 32 }
408
409 32 void printInputs() {
410
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 32 BOOST_TEST_MESSAGE("----- inputs -----");
411 // BOOST_TEST_MESSAGE("feature_.errorSIN: " <<
412 // feature_.errorSIN(time_).transpose());
413
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 32 BOOST_TEST_MESSAGE("task.controlGain: " << task_.controlGainSIN(time_));
414 32 }
415
416 32 void recompute() {
417
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 32 FeatureTestBase::recompute();
418
419
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 32 const SE3 oMfb = data_.oMf[fb_], oMfa = data_.oMf[fa_],
420
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 32 faMfb(feature_.faMfb.accessCopy().matrix()),
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 32 faMfbDes(feature_.faMfbDes.accessCopy().matrix());
422
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 32 const Vector &nu(feature_.faNufafbDes.accessCopy());
423
424
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 32 computeExpectedTaskOutput(
425 toVector(oMfa.inverse() * oMfb), toVector(faMfbDes),
426 (boost::is_same<LieGroup_t, SE3_t>::value
427
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 32 ? faMfbDes.toActionMatrixInverse() * nu
428
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 32 : (Vector6d() << nu.head<3>() -
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 48 faMfb.translation().cross(nu.tail<3>()),
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 32 faMfbDes.rotation().transpose() * nu.tail<3>())
431 16 .finished()),
432
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 32 LieGroup_t());
433
434
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 32 checkTaskOutput();
435 32 }
436
437 32 void printOutputs() {
438
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 32 BOOST_TEST_MESSAGE("----- output -----");
439
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 32 BOOST_TEST_MESSAGE("time: " << time_);
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 32 BOOST_TEST_MESSAGE(
441 "feature.errorSOUT: " << feature_.errorSOUT(time_).transpose());
442
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 32 BOOST_TEST_MESSAGE(
443 "feature.errordotSOUT: " << feature_.errordotSOUT(time_).transpose());
444
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 32 BOOST_TEST_MESSAGE("task.taskSOUT: " << task_.taskSOUT(time_));
445 // BOOST_TEST_MESSAGE("task.errorSOUT: " << task_.errorSOUT(time_));
446 // BOOST_TEST_MESSAGE("task.errordtSOUT: " <<
447 // task_.errorTimeDerivativeSOUT(time_));
448
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 32 BOOST_TEST_MESSAGE(
449 "Expected task output: " << expectedTaskOutput_.transpose());
450 32 }
451
452 32 virtual void checkJacobian() {
453 32 time_++;
454 // We want to check that e (q+dq, t) ~ e(q, t) + de/dq(q,t) dq
455
456
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 32 setGain(1.);
457
458
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 32 Vector q(pinocchio::randomConfiguration(model_));
459
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 32 pinocchio::framesForwardKinematics(model_, data_, q);
460
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 32 pinocchio::computeJointJacobians(model_, data_);
461
462 // Poses
463
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 32 setSignal(feature_.oMjb,
464
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 32 MatrixHomogeneous(data_.oMi[jb_].toHomogeneousMatrix()));
465
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 32 if (relative_) {
466
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 16 setSignal(feature_.oMja,
467
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 32 MatrixHomogeneous(data_.oMi[ja_].toHomogeneousMatrix()));
468 }
469
470 // Jacobians
471
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 32 Matrix J(6, model_.nv);
472
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 32 J.setZero();
473
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 32 pinocchio::getJointJacobian(model_, data_, jb_, pinocchio::LOCAL, J);
474
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 32 setSignal(feature_.jbJjb, J);
475
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 32 if (relative_) {
476
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 16 J.setZero();
477
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 16 pinocchio::getJointJacobian(model_, data_, ja_, pinocchio::LOCAL, J);
478
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 16 setSignal(feature_.jaJja, J);
479 }
480
481 // Desired
482
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 32 setSignal(feature_.faMfbDes, randomM());
483
484 // Get task jacobian
485
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 32 Vector e_q = task_.errorSOUT.access(time_);
486
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 32 J = task_.jacobianSOUT.access(time_);
487
488
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 64 Eigen::IOFormat PyVectorFmt(Eigen::FullPrecision, 0, ", ", ", ", "", "",
489 "[", "]\n");
490
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 64 Eigen::IOFormat PyMatrixFmt(Eigen::FullPrecision, 0, ", ", ",\n", "[", "]",
491 "[", "]\n");
492
493
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 32 Vector qdot(Vector::Zero(model_.nv));
494 32 double eps = 1e-6;
495
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 32 BOOST_TEST_MESSAGE(
496 data_.oMi[ja_].toHomogeneousMatrix().format(PyMatrixFmt)
497 << data_.oMi[jb_].toHomogeneousMatrix().format(PyMatrixFmt)
498 << model_.frames[fa_].placement.toHomogeneousMatrix().format(
499 PyMatrixFmt)
500 << model_.frames[fb_].placement.toHomogeneousMatrix().format(
501 PyMatrixFmt)
502 << J.format(PyMatrixFmt));
503
504
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 1120 for (int i = 0; i < model_.nv; ++i) {
505 1088 time_++;
506
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 1088 qdot(i) = eps;
507
508 // Update pose signals
509
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 1088 Vector q_qdot = pinocchio::integrate(model_, q, qdot);
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 1088 pinocchio::framesForwardKinematics(model_, data_, q_qdot);
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 1088 MatrixHomogeneous(data_.oMi[jb_].toHomogeneousMatrix()));
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 1088 if (relative_) {
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 544 setSignal(feature_.oMja,
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 1088 MatrixHomogeneous(data_.oMi[ja_].toHomogeneousMatrix()));
516 }
517
518 // Recompute output and check finite diff
519
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 1088 Vector e_q_dq = task_.errorSOUT.access(time_);
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 1088 BOOST_CHECK_MESSAGE(
521 (((e_q_dq - e_q) / eps) - J.col(i)).maxCoeff() < 1e-4,
522 "Jacobian col " << i << " does not match finite difference.\n"
523 << ((e_q_dq - e_q) / eps).format(PyVectorFmt) << '\n'
524 << J.col(i).format(PyVectorFmt) << '\n');
525
526
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 1088 qdot(i) = 0.;
527 }
528 32 time_++;
529 32 }
530
531 32 virtual void checkFeedForward() {
532
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 32 setGain(0.);
533 // random config
534 // set inputs
535 // compute e = task_.taskSOUT and J = task_.jacobianSOUT
536 // check that e (q + eps*qdot) - e (q) ~= eps * J * qdot
537 // compute qdot = J^+ * e
538 // check that faMfb (q+eps*qdot) ~= faMfb(q) + eps * faNufafbDes
539
540 32 time_++;
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 32 Vector q(pinocchio::randomConfiguration(model_));
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 32 pinocchio::framesForwardKinematics(model_, data_, q);
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 32 pinocchio::computeJointJacobians(model_, data_);
544
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 32 SE3 faMfb = data_.oMf[fa_].actInv(data_.oMf[fb_]);
546
547 // Poses
548
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 32 setSignal(feature_.oMjb,
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 32 MatrixHomogeneous(data_.oMi[jb_].toHomogeneousMatrix()));
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 32 if (relative_) {
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 16 setSignal(feature_.oMja,
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 32 MatrixHomogeneous(data_.oMi[ja_].toHomogeneousMatrix()));
553 }
554
555 // Jacobians
556
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 32 Matrix J(6, model_.nv);
557
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 32 if (relative_) {
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 16 J.setZero();
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 16 setSignal(feature_.jaJja, J);
564 }
565
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 32 Matrix faJfafb;
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 32 J.setZero();
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 32 J.setZero();
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 32 pinocchio::getFrameJacobian(model_, data_, fa_, pinocchio::LOCAL, J);
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 32 faJfafb -= J;
573
574 // Desired
575
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 32 setSignal(feature_.faMfbDes, randomM());
576
577 // Get task jacobian
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 32 J = task_.jacobianSOUT.accessCopy();
580
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 32 Eigen::JacobiSVD<Matrix> svd(J, Eigen::ComputeThinU | Eigen::ComputeThinV);
581
582
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 32 Vector faNufafbDes(Vector::Zero(6));
583 32 double eps = 1e-6;
584
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 224 for (int i = 0; i < 6; ++i) {
585 192 time_++;
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 192 setSignal(feature_.faNufafbDes, faNufafbDes);
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 192 task_.taskSOUT.recompute(time_);
589
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 192 Vector qdot = svd.solve(toVector(task_.taskSOUT.accessCopy()));
591
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 192 EIGEN_VECTOR_IS_APPROX(faNufafbDes, faNufafb, eps);
594
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 192 faNufafbDes(i) = 0.;
596 }
597 32 time_++;
598 32 }
599 };
600
601 template <typename TestClass>
602 16 void runTest(TestClass &runner, int N = 2)
603 // int N = 10)
604 {
605
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 48 for (int i = 0; i < N; ++i) runner.checkValue();
606
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607
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 48 for (int i = 0; i < N; ++i) runner.checkFeedForward();
608 16 }
609
610 BOOST_AUTO_TEST_SUITE(feature_pose)
611
612 template <Representation_t representation>
613 4 void feature_pose_absolute_tpl(const std::string &repr) {
614
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615
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617
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618
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621 4 }
622
623 BOOST_AUTO_TEST_SUITE(absolute)
624
625
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 4 BOOST_AUTO_TEST_CASE(r3xso3) {
626
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 2 feature_pose_absolute_tpl<R3xSO3Representation>("R3xSO3");
627 2 }
628
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 4 BOOST_AUTO_TEST_CASE(se3) {
630
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 2 feature_pose_absolute_tpl<SE3Representation>("SE3");
631 2 }
632
633 BOOST_AUTO_TEST_SUITE_END() // absolute
634
635 template <Representation_t representation>
636 4 void feature_pose_relative_tpl(const std::string &repr) {
637
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640
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643 4 }
644
645 BOOST_AUTO_TEST_SUITE(relative)
646
647
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 2 feature_pose_relative_tpl<R3xSO3Representation>("R3xSO3");
649 2 }
650
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652
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 2 feature_pose_relative_tpl<SE3Representation>("SE3");
653 2 }
654
655 BOOST_AUTO_TEST_SUITE_END() // relative
656
657 BOOST_AUTO_TEST_SUITE_END() // feature_pose
658