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Fixed sign issue on nonlinear constraints, so that that it is not necessary to flip the sign on the delta configs before using exmap.

release/4.3a0
Alex Cunningham 2009-11-28 21:00:09 +00:00
parent 0ff7e3a5d9
commit 395e4ae3f1
5 changed files with 78 additions and 24 deletions
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4
cpp/NonlinearConstraint-inl.h
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@ -86,7 +86,7 @@ NonlinearConstraint1<Config>::linearize(const Config& config, const VectorConfig
GaussianFactor(key_, A1, this->lagrange_key_, eye(this->p_), zero(this->p_), 1.0));
// construct the constraint
GaussianFactor::shared_ptr constraint(new GaussianFactor(key_, grad, g, 0.0));
GaussianFactor::shared_ptr constraint(new GaussianFactor(key_, grad, -1*g, 0.0));
return std::make_pair(factor, constraint);
}
@ -163,7 +163,7 @@ NonlinearConstraint2<Config>::linearize(const Config& config, const VectorConfig
this->lagrange_key_, eye(this->p_), zero(this->p_), 1.0));
// construct the constraint
GaussianFactor::shared_ptr constraint(new GaussianFactor(key1_, grad1, key2_, grad2, g, 0.0));
GaussianFactor::shared_ptr constraint(new GaussianFactor(key1_, grad1, key2_, grad2, -1*g, 0.0));
return std::make_pair(factor, constraint);
}

2
cpp/SQPOptimizer-inl.h
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@ -96,7 +96,7 @@ SQPOptimizer<G, C> SQPOptimizer<G, C>::iterate(Verbosity v) const {
if (verbose) fg.print("Before Optimization");
// optimize linear graph to get full delta config
VectorConfig delta = fg.optimize(full_ordering_.subtract(rem)).scale(-1.0);
VectorConfig delta = fg.optimize(full_ordering_.subtract(rem));
if (verbose) delta.print("Delta Config");

6
cpp/testNonlinearConstraint.cpp
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@ -66,7 +66,7 @@ TEST( NonlinearConstraint1, unary_scalar_linearize ) {
// verify
GaussianFactor expFactor("x", Matrix_(1,1, 6.0), "L_x1", eye(1), zero(1), 1.0);
GaussianFactor expConstraint("x", Matrix_(1,1, 2.0), Vector_(1,-4.0), 0.0);
GaussianFactor expConstraint("x", Matrix_(1,1, 2.0), Vector_(1, 4.0), 0.0);
CHECK(assert_equal(*actFactor, expFactor));
CHECK(assert_equal(*actConstraint, expConstraint));
}
@ -159,7 +159,7 @@ TEST( NonlinearConstraint2, binary_scalar_linearize ) {
"L_xy", eye(1), zero(1), 1.0);
GaussianFactor expConstraint("x", Matrix_(1,1, 2.0),
"y", Matrix_(1,1, -1.0),
Vector_(1,-6.0), 0.0);
Vector_(1, 6.0), 0.0);
CHECK(assert_equal(*actFactor, expFactor));
CHECK(assert_equal(*actConstraint, expConstraint));
}
@ -248,7 +248,7 @@ TEST( NonlinearConstraint1, unary_inequality_linearize ) {
// verify
GaussianFactor expFactor("x", Matrix_(1,1, 6.0), "L_x", eye(1), zero(1), 1.0);
GaussianFactor expConstraint("x", Matrix_(1,1, 2.0), Vector_(1,-4.0), 0.0);
GaussianFactor expConstraint("x", Matrix_(1,1, 2.0), Vector_(1, 4.0), 0.0);
CHECK(assert_equal(*actFactor2, expFactor));
CHECK(assert_equal(*actConstraint2, expConstraint));
}

6
cpp/testSQP.cpp
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@ -223,11 +223,11 @@ TEST (SQP, problem1_sqp ) {
// solve
Ordering ord;
ord += "x", "y", "lam";
VectorConfig delta = fg.optimize(ord).scale(-1.0); // flip sign
VectorConfig delta = fg.optimize(ord);
if (verbose) delta.print("Delta");
// update initial estimate
VectorConfig newState = state.exmap(delta);
VectorConfig newState = state.exmap(delta.scale(-1.0));
// set the state to the updated state
state = newState;
@ -434,7 +434,7 @@ TEST (SQP, two_pose ) {
ordering += "x1", "x2", "l1", "l2", "L_l1l2", "L_x1";
// optimize linear graph to get full delta config
VectorConfig delta = fg.optimize(ordering).scale(-1.0);
VectorConfig delta = fg.optimize(ordering);
if (verbose) delta.print("Delta Config");
// update both state variables

84
cpp/testSQPOptimizer.cpp
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@ -15,10 +15,12 @@
#include "NonlinearEquality.h"
#include "VectorConfig.h"
#include "Ordering.h"
#include "NonlinearOptimizer.h"
#include "SQPOptimizer.h"
// implementations
#include "NonlinearConstraint-inl.h"
#include "NonlinearOptimizer-inl.h"
#include "SQPOptimizer-inl.h"
using namespace std;
@ -157,7 +159,7 @@ TEST ( SQPOptimizer, map_warp_initLam ) {
expected.insert("l1", Vector_(2, 1.0, 6.0));
expected.insert("l2", Vector_(2, 1.0, 6.0));
expected.insert("x2", Vector_(2, 5.0, 6.0));
CHECK(assert_equal(actual, expected));
CHECK(assert_equal(expected, actual));
}
/* ********************************************************************* */
@ -191,7 +193,7 @@ TEST ( SQPOptimizer, map_warp ) {
expected.insert("l1", Vector_(2, 1.0, 6.0));
expected.insert("l2", Vector_(2, 1.0, 6.0));
expected.insert("x2", Vector_(2, 5.0, 6.0));
CHECK(assert_equal(actual, expected));
CHECK(assert_equal(expected, actual));
}
/* ********************************************************************* */
@ -235,13 +237,16 @@ Vector g_func(const VectorConfig& config, const list<string>& keys) {
/** gradient at pose */
Matrix grad_g1(const VectorConfig& config, const list<string>& keys) {
Matrix grad;
return grad;
Vector x2 = config[keys.front()], obs = config[keys.back()];
Vector grad = 2.0*(x2-obs);
return Matrix_(1,2, grad(0), grad(1));
}
/** gradient at obstacle */
Matrix grad_g2(const VectorConfig& config, const list<string>& keys) {
return -1*eye(1);
Vector x2 = config[keys.front()], obs = config[keys.back()];
Vector grad = -2.0*(x2-obs);
return Matrix_(1,2, grad(0), grad(1));
}
}
@ -276,13 +281,59 @@ pair<NLGraph, VectorConfig> obstacleAvoidGraph() {
graph.push_back(e1);
graph.push_back(e2);
graph.push_back(e3);
graph.push_back(c1);
//graph.push_back(c1);
graph.push_back(f1);
graph.push_back(f2);
return make_pair(graph, feasible);
}
/* ********************************************************************* */
TEST ( SQPOptimizer, trajectory_shortening ) {
// fix start, end positions
VectorConfig feasible;
feasible.insert("x1", Vector_(2, 0.0, 0.0));
feasible.insert("x3", Vector_(2, 10.0, 0.0));
feasible.insert("obs", Vector_(2, 5.0, -0.5));
shared_NLE e1(new NLE("x1", feasible, 2, *vector_compare));
shared_NLE e2(new NLE("x3", feasible, 2, *vector_compare));
shared_NLE e3(new NLE("obs", feasible, 2, *vector_compare));
// measurement from x1 to x2
Vector x1x2 = Vector_(2, 5.0, 0.0);
double sigma1 = 0.1;
shared f1(new Simulated2DOdometry(x1x2, sigma1, "x1", "x2"));
// measurement from x2 to x3
Vector x2x3 = Vector_(2, 5.0, 0.0);
double sigma2 = 0.1;
shared f2(new Simulated2DOdometry(x2x3, sigma2, "x2", "x3"));
// construct the graph
NLGraph graph;
graph.push_back(e1);
graph.push_back(e2);
graph.push_back(e3);
graph.push_back(f1);
graph.push_back(f2);
// optimize
typedef NonlinearOptimizer<NLGraph, VectorConfig> Optimizer;
//typedef SQPOptimizer<NLGraph, VectorConfig> Optimizer;
Ordering ordering;
ordering += "x1", "x2", "x3", "obs";
shared_config config(new VectorConfig(feasible));
config->insert("x2", Vector_(2, 5.0, 10.0));
Optimizer optimizer(graph, ordering, config);
// perform iteration
Optimizer afterOneIteration = optimizer.iterate();
// print for evaluation
// config->print("Initial config");
// afterOneIteration.config()->print("Config after optimization");
}
/* ********************************************************************* */
TEST ( SQPOptimizer, inequality_inactive ) {
// create the graph
@ -291,7 +342,7 @@ TEST ( SQPOptimizer, inequality_inactive ) {
// create the rest of the config
shared_config init(new VectorConfig(feasible));
init->insert("x2", Vector_(2, 5.0, 0.6));
init->insert("x2", Vector_(2, 5.0, 100.0));
// create an ordering
Ordering ord;
@ -301,18 +352,21 @@ TEST ( SQPOptimizer, inequality_inactive ) {
Optimizer optimizer(graph, ord, init);
// perform optimization
// FIXME: avoidance constraint not correctly implemented
//Optimizer afterOneIteration = optimizer.iterate(Optimizer::SILENT);
Optimizer afterOneIteration = optimizer.iterate(Optimizer::SILENT);
// print for evaluation
// init->print("Before SQP iteration");
// afterOneIteration.config()->print("After SQP iteration");
}
/* ********************************************************************* */
TEST ( SQPOptimizer, inequality_active ) {
// create the graph
NLGraph graph; VectorConfig feasible;
boost::tie(graph, feasible) = obstacleAvoidGraph();
}
//TEST ( SQPOptimizer, inequality_active ) {
// // create the graph
// NLGraph graph; VectorConfig feasible;
// boost::tie(graph, feasible) = obstacleAvoidGraph();
//
//}
/* ************************************************************************* */