/* ************************************************************************* */
void NonlinearISAM::update(const NonlinearFactorGraph& newFactors,
const Values& initialValues) {
if(newFactors.size() > 0) {
// Reorder and relinearize every reorderInterval updates
if(reorderInterval_ > 0 && ++reorderCounter_ >= reorderInterval_) {
reorder_relinearize();
reorderCounter_ = 0;
}
factors_.push_back(newFactors);
// Linearize new factors and insert them
// TODO: optimize for whole config?
linPoint_.insert(initialValues);
// Augment ordering
// TODO: allow for ordering constraints within the new variables
// FIXME: should just loop over new values
BOOST_FOREACH(const NonlinearFactorGraph::sharedFactor& factor, newFactors)
BOOST_FOREACH(Key key, factor->keys())
ordering_.tryInsert(key, ordering_.nVars()); // will do nothing if already present
boost::shared_ptr<GaussianFactorGraph> linearizedNewFactors = newFactors.linearize(linPoint_, ordering_);
// Update ISAM
isam_.update(*linearizedNewFactors);
}
}
/* ************************************************************************* */
void NonlinearISAM::update(const NonlinearFactorGraph& newFactors, const Values& initialValues) {
if(newFactors.size() > 0) {
// Reorder and relinearize every reorderInterval updates
if(reorderInterval_ > 0 && ++reorderCounter_ >= reorderInterval_) {
reorder_relinearize();
reorderCounter_ = 0;
}
factors_.push_back(newFactors);
// Linearize new factors and insert them
// TODO: optimize for whole config?
linPoint_.insert(initialValues);
boost::shared_ptr<GaussianFactorGraph> linearizedNewFactors = newFactors.linearize(linPoint_);
// Update ISAM
isam_.update(*linearizedNewFactors, eliminationFunction_);
}
}