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going to build the linear factor graph with regularized measurements

release/4.3a0
Luca 2014-05-16 12:16:06 -04:00
parent d942e34ede
commit a0d0243e36
1 changed files with 50 additions and 6 deletions
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56
examples/tests/testPlanarSLAMExample_lago.cpp
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@ -23,6 +23,8 @@
// the robot positions and Point2 variables (x, y) to represent the landmark coordinates.
#include <gtsam/geometry/Pose2.h>
#include <gtsam/linear/GaussianFactorGraph.h>
// Each variable in the system (poses and landmarks) must be identified with a unique key.
// We can either use simple integer keys (1, 2, 3, ...) or symbols (X1, X2, L1).
// Here we will use Symbols
@ -205,7 +207,7 @@ map<Key, double> computeThetasToRoot(vector<Key>& keysInBinary, map<Key, double>
}
/* *************************************************************************** */
TEST( Lago, sumOverLoops ) {
TEST( Lago, orientationsOverSpanningTree ) {
NonlinearFactorGraph g = simple::graph();
PredecessorMap<Key> tree = findMinimumSpanningTree<NonlinearFactorGraph, Key,
BetweenFactor<Pose2> >(g);
@ -222,7 +224,45 @@ TEST( Lago, sumOverLoops ) {
expected[x2]= -PI; // edge x0->x2 (traversed backwards wrt edge (x2,x0))
expected[x3]= -PI/2; // edge x0->x3 (consistent with edge (x0,x3))
vector<Key> keysInBinary;
map<Key, double> deltaThetaMap;
vector<size_t> spanningTree; // ids of between factors forming the spanning tree T
vector<size_t> chords; // ids of between factors corresponding to chords wrt T
getSymbolicSubgraph(keysInBinary, spanningTree, chords, deltaThetaMap, tree, g);
map<Key, double> actual;
actual = computeThetasToRoot(keysInBinary, deltaThetaMap, tree);
DOUBLES_EQUAL(expected[x0], actual[x0], 1e-6);
DOUBLES_EQUAL(expected[x1], actual[x1], 1e-6);
DOUBLES_EQUAL(expected[x2], actual[x2], 1e-6);
DOUBLES_EQUAL(expected[x3], actual[x3], 1e-6);
}
/*
* Linear factor graph with regularized orientation measurements
*/
GaussianFactorGraph buildOrientationGraph(const vector<size_t>& spanningTree, const vector<size_t>& chords,
const NonlinearFactorGraph& g, map<Key, double> orientationsToRoot){
GaussianFactorGraph lagoGraph;
BOOST_FOREACH(const size_t& factorId, spanningTree){ // put original measurements in the spanning tree
Key key1 = g[factorId]->keys()[0];
Key key2 = g[factorId]->keys()[1];
boost::shared_ptr< BetweenFactor<Pose2> > pose2Between = boost::dynamic_pointer_cast< BetweenFactor<Pose2> >(g[factorId]);
if (!pose2Between) throw std::invalid_argument("buildOrientationGraph: invalid between factor!");;
double deltaTheta = pose2Between->measured().theta();
//SharedNoiseModel model = g[factorId]->get_noiseModel()
//lagoGraph.add(JacobianFactor(key1, -1, key2, 1, deltaTheta, model));
}
return lagoGraph;
}
/* *************************************************************************** */
TEST( Lago, sumOverLoops ) {
NonlinearFactorGraph g = simple::graph();
PredecessorMap<Key> tree = findMinimumSpanningTree<NonlinearFactorGraph, Key,
BetweenFactor<Pose2> >(g);
vector<Key> keysInBinary;
map<Key, double> deltaThetaMap;
@ -230,11 +270,15 @@ TEST( Lago, sumOverLoops ) {
vector<size_t> chords; // ids of between factors corresponding to chords wrt T
getSymbolicSubgraph(keysInBinary, spanningTree, chords, deltaThetaMap, tree, g);
actual = computeThetasToRoot(keysInBinary, deltaThetaMap, tree);
DOUBLES_EQUAL(expected[x0], actual[x0], 1e-6);
DOUBLES_EQUAL(expected[x1], actual[x1], 1e-6);
DOUBLES_EQUAL(expected[x2], actual[x2], 1e-6);
DOUBLES_EQUAL(expected[x3], actual[x3], 1e-6);
map<Key, double> orientationsToRoot = computeThetasToRoot(keysInBinary, deltaThetaMap, tree);
GaussianFactorGraph lagoGraph = buildOrientationGraph(spanningTree, chords, g, orientationsToRoot);
// Vector2 expected;
// expected[0]= 0.0;
// expected[1]= 0.0;
// DOUBLES_EQUAL(expected[x0], actual[x0], 1e-6);
// DOUBLES_EQUAL(expected[x1], actual[x1], 1e-6);
}
/* *************************************************************************** */