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gtsam/gtsam_unstable/nonlinear/BatchFixedLagSmoother.cpp

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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file BatchFixedLagSmoother.cpp
* @brief An LM-based fixed-lag smoother.
*
* @author Michael Kaess, Stephen Williams
* @date Oct 14, 2012
*/
#include <gtsam_unstable/nonlinear/BatchFixedLagSmoother.h>
#include <gtsam/nonlinear/LinearContainerFactor.h>
#include <gtsam/linear/GaussianJunctionTree.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/GaussianFactor.h>
#include <gtsam/inference/inference.h>
#include <gtsam/base/debug.h>
namespace gtsam {
/* ************************************************************************* */
void BatchFixedLagSmoother::print(const std::string& s, const KeyFormatter& keyFormatter) const {
FixedLagSmoother::print(s, keyFormatter);
// TODO: What else to print?
}
/* ************************************************************************* */
bool BatchFixedLagSmoother::equals(const FixedLagSmoother& rhs, double tol) const {
const BatchFixedLagSmoother* e = dynamic_cast<const BatchFixedLagSmoother*> (&rhs);
return e != NULL
&& FixedLagSmoother::equals(*e, tol)
&& factors_.equals(e->factors_, tol)
&& theta_.equals(e->theta_, tol);
}
/* ************************************************************************* */
FixedLagSmoother::Result BatchFixedLagSmoother::update(const NonlinearFactorGraph& newFactors, const Values& newTheta, const KeyTimestampMap& timestamps) {
const bool debug = ISDEBUG("BatchFixedLagSmoother update");
if(debug) {
std::cout << "BatchFixedLagSmoother::update() START" << std::endl;
}
// Add the new factors
insertFactors(newFactors);
// Add the new variables
theta_.insert(newTheta);
// Add new variables to the end of the ordering
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, newTheta) {
ordering_.push_back(key_value.key);
}
// Augment Delta
std::vector<size_t> dims;
dims.reserve(newTheta.size());
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, newTheta) {
dims.push_back(key_value.value.dim());
}
delta_.append(dims);
for(size_t i = delta_.size() - dims.size(); i < delta_.size(); ++i) {
delta_[i].setZero();
}
// Update the Timestamps associated with the factor keys
updateKeyTimestampMap(timestamps);
// Get current timestamp
double current_timestamp = getCurrentTimestamp();
if(debug) std::cout << "Current Timestamp: " << current_timestamp << std::endl;
// Find the set of variables to be marginalized out
std::set<Key> marginalizableKeys = findKeysBefore(current_timestamp - smootherLag_);
if(debug) {
std::cout << "Marginalizable Keys: ";
BOOST_FOREACH(Key key, marginalizableKeys) {
std::cout << DefaultKeyFormatter(key) << " ";
}
std::cout << std::endl;
}
// Reorder
reorder(marginalizableKeys);
// Optimize
Result result;
if(theta_.size() > 0) {
result = optimize();
}
// Marginalize out old variables.
if(marginalizableKeys.size() > 0) {
marginalize(marginalizableKeys);
}
if(debug) {
std::cout << "BatchFixedLagSmoother::update() FINISH" << std::endl;
}
return result;
}
/* ************************************************************************* */
void BatchFixedLagSmoother::insertFactors(const NonlinearFactorGraph& newFactors) {
BOOST_FOREACH(const NonlinearFactor::shared_ptr& factor, newFactors) {
Index index;
// Insert the factor into an existing hole in the factor graph, if possible
if(availableSlots_.size() > 0) {
index = availableSlots_.front();
availableSlots_.pop();
factors_.replace(index, factor);
} else {
index = factors_.size();
factors_.push_back(factor);
}
// Update the FactorIndex
BOOST_FOREACH(Key key, *factor) {
factorIndex_[key].insert(index);
}
}
}
/* ************************************************************************* */
void BatchFixedLagSmoother::removeFactors(const std::set<size_t>& deleteFactors) {
BOOST_FOREACH(size_t slot, deleteFactors) {
if(factors_.at(slot)) {
// Remove references to this factor from the FactorIndex
BOOST_FOREACH(Key key, *(factors_.at(slot))) {
factorIndex_[key].erase(slot);
}
// Remove the factor from the factor graph
factors_.remove(slot);
// Add the factor's old slot to the list of available slots
availableSlots_.push(slot);
} else {
// TODO: Throw an error??
std::cout << "Attempting to remove a factor from slot " << slot << ", but it is already NULL." << std::endl;
}
}
}
/* ************************************************************************* */
void BatchFixedLagSmoother::eraseKeys(const std::set<Key>& keys) {
BOOST_FOREACH(Key key, keys) {
// Erase the key from the values
theta_.erase(key);
// Erase the key from the factor index
factorIndex_.erase(key);
// Erase the key from the set of linearized keys
if(linearKeys_.exists(key)) {
linearKeys_.erase(key);
}
}
eraseKeyTimestampMap(keys);
// Permute the ordering such that the removed keys are at the end.
// This is a prerequisite for removing them from several structures
std::vector<Index> toBack;
BOOST_FOREACH(Key key, keys) {
toBack.push_back(ordering_.at(key));
}
Permutation forwardPermutation = Permutation::PushToBack(toBack, ordering_.size());
ordering_.permuteInPlace(forwardPermutation);
delta_.permuteInPlace(forwardPermutation);
// Remove marginalized keys from the ordering and delta
for(size_t i = 0; i < keys.size(); ++i) {
ordering_.pop_back();
delta_.pop_back();
}
}
/* ************************************************************************* */
void BatchFixedLagSmoother::reorder(const std::set<Key>& marginalizeKeys) {
// Calculate a variable index
VariableIndexOrdered variableIndex(*factors_.symbolic(ordering_), ordering_.size());
// COLAMD groups will be used to place marginalize keys in Group 0, and everything else in Group 1
int group0 = 0;
int group1 = marginalizeKeys.size() > 0 ? 1 : 0;
// Initialize all variables to group1
std::vector<int> cmember(variableIndex.size(), group1);
// Set all of the marginalizeKeys to Group0
if(marginalizeKeys.size() > 0) {
BOOST_FOREACH(Key key, marginalizeKeys) {
cmember[ordering_.at(key)] = group0;
}
}
// Generate the permutation
Permutation forwardPermutation = *inference::PermutationCOLAMD_(variableIndex, cmember);
// Permute the ordering, variable index, and deltas
ordering_.permuteInPlace(forwardPermutation);
delta_.permuteInPlace(forwardPermutation);
}
/* ************************************************************************* */
FixedLagSmoother::Result BatchFixedLagSmoother::optimize() {
// Create output result structure
Result result;
result.nonlinearVariables = theta_.size() - linearKeys_.size();
result.linearVariables = linearKeys_.size();
// Set optimization parameters
double lambda = parameters_.lambdaInitial;
double lambdaFactor = parameters_.lambdaFactor;
double lambdaUpperBound = parameters_.lambdaUpperBound;
double lambdaLowerBound = 0.5 / parameters_.lambdaUpperBound;
size_t maxIterations = parameters_.maxIterations;
double relativeErrorTol = parameters_.relativeErrorTol;
double absoluteErrorTol = parameters_.absoluteErrorTol;
double errorTol = parameters_.errorTol;
// Create a Values that holds the current evaluation point
Values evalpoint = theta_.retract(delta_, ordering_);
result.error = factors_.error(evalpoint);
// Use a custom optimization loop so the linearization points can be controlled
double previousError;
VectorValuesOrdered newDelta;
do {
previousError = result.error;
// Do next iteration
gttic(optimizer_iteration);
{
// Linearize graph around the linearization point
GaussianFactorGraphOrdered linearFactorGraph = *factors_.linearize(theta_, ordering_);
// Keep increasing lambda until we make make progress
while(true) {
// Add prior factors at the current solution
gttic(damp);
GaussianFactorGraphOrdered dampedFactorGraph(linearFactorGraph);
dampedFactorGraph.reserve(linearFactorGraph.size() + delta_.size());
{
// for each of the variables, add a prior at the current solution
double sigma = 1.0 / std::sqrt(lambda);
for(size_t j=0; j<delta_.size(); ++j) {
size_t dim = delta_[j].size();
Matrix A = eye(dim);
Vector b = delta_[j];
SharedDiagonal model = noiseModel::Isotropic::Sigma(dim, sigma);
GaussianFactorOrdered::shared_ptr prior(new JacobianFactorOrdered(j, A, b, model));
dampedFactorGraph.push_back(prior);
}
}
gttoc(damp);
result.intermediateSteps++;
gttic(solve);
// Solve Damped Gaussian Factor Graph
newDelta = GaussianJunctionTreeOrdered(dampedFactorGraph).optimize(parameters_.getEliminationFunction());
// update the evalpoint with the new delta
evalpoint = theta_.retract(newDelta, ordering_);
gttoc(solve);
// Evaluate the new error
gttic(compute_error);
double error = factors_.error(evalpoint);
gttoc(compute_error);
if(error < result.error) {
// Keep this change
// Update the error value
result.error = error;
// Update the linearization point
theta_ = evalpoint;
// Reset the deltas to zeros
delta_.setZero();
// Put the linearization points and deltas back for specific variables
if(enforceConsistency_ && (linearKeys_.size() > 0)) {
theta_.update(linearKeys_);
BOOST_FOREACH(const Values::ConstKeyValuePair& key_value, linearKeys_) {
Index index = ordering_.at(key_value.key);
delta_.at(index) = newDelta.at(index);
}
}
// Decrease lambda for next time
lambda /= lambdaFactor;
if(lambda < lambdaLowerBound) {
lambda = lambdaLowerBound;
}
// End this lambda search iteration
break;
} else {
// Reject this change
// Increase lambda and continue searching
lambda *= lambdaFactor;
if(lambda > lambdaUpperBound) {
// The maximum lambda has been used. Print a warning and end the search.
std::cout << "Warning: Levenberg-Marquardt giving up because cannot decrease error with maximum lambda" << std::endl;
break;
}
}
} // end while
}
gttoc(optimizer_iteration);
result.iterations++;
} while(result.iterations < maxIterations &&
!checkConvergence(relativeErrorTol, absoluteErrorTol, errorTol, previousError, result.error, NonlinearOptimizerParams::SILENT));
return result;
}
/* ************************************************************************* */
void BatchFixedLagSmoother::marginalize(const std::set<Key>& marginalizeKeys) {
// In order to marginalize out the selected variables, the factors involved in those variables
// must be identified and removed. Also, the effect of those removed factors on the
// remaining variables needs to be accounted for. This will be done with linear container factors
// from the result of a partial elimination. This function removes the marginalized factors and
// adds the linearized factors back in.
// Calculate marginal factors on the remaining variables (after marginalizing 'marginalizeKeys')
// Note: It is assumed the ordering already has these keys first
// Create the linear factor graph
GaussianFactorGraphOrdered linearFactorGraph = *factors_.linearize(theta_, ordering_);
// Create a variable index
VariableIndexOrdered variableIndex(linearFactorGraph, ordering_.size());
// Use the variable Index to mark the factors that will be marginalized
std::set<size_t> removedFactorSlots;
BOOST_FOREACH(Key key, marginalizeKeys) {
const FastList<size_t>& slots = variableIndex[ordering_.at(key)];
removedFactorSlots.insert(slots.begin(), slots.end());
}
// Construct an elimination tree to perform sparse elimination
std::vector<EliminationForest::shared_ptr> forest( EliminationForest::Create(linearFactorGraph, variableIndex) );
// This is a tree. Only the top-most nodes/indices need to be eliminated; all of the children will be eliminated automatically
// Find the subset of nodes/keys that must be eliminated
std::set<Index> indicesToEliminate;
BOOST_FOREACH(Key key, marginalizeKeys) {
indicesToEliminate.insert(ordering_.at(key));
}
BOOST_FOREACH(Key key, marginalizeKeys) {
EliminationForest::removeChildrenIndices(indicesToEliminate, forest.at(ordering_.at(key)));
}
// Eliminate each top-most key, returning a Gaussian Factor on some of the remaining variables
// Convert the marginal factors into Linear Container Factors
// Add the marginal factor variables to the separator
NonlinearFactorGraph marginalFactors;
BOOST_FOREACH(Index index, indicesToEliminate) {
GaussianFactorOrdered::shared_ptr gaussianFactor = forest.at(index)->eliminateRecursive(parameters_.getEliminationFunction());
if(gaussianFactor->size() > 0) {
LinearContainerFactor::shared_ptr marginalFactor(new LinearContainerFactor(gaussianFactor, ordering_, theta_));
marginalFactors.push_back(marginalFactor);
// Add the keys associated with the marginal factor to the separator values
BOOST_FOREACH(Key key, *marginalFactor) {
if(!linearKeys_.exists(key)) {
linearKeys_.insert(key, theta_.at(key));
}
}
}
}
insertFactors(marginalFactors);
// Remove the marginalized variables and factors from the filter
// Remove marginalized factors from the factor graph
removeFactors(removedFactorSlots);
// Remove marginalized keys from the system
eraseKeys(marginalizeKeys);
}
/* ************************************************************************* */
void BatchFixedLagSmoother::PrintKeySet(const std::set<Key>& keys, const std::string& label) {
std::cout << label;
BOOST_FOREACH(gtsam::Key key, keys) {
std::cout << " " << gtsam::DefaultKeyFormatter(key);
}
std::cout << std::endl;
}
/* ************************************************************************* */
void BatchFixedLagSmoother::PrintSymbolicFactor(const NonlinearFactor::shared_ptr& factor) {
std::cout << "f(";
if(factor) {
BOOST_FOREACH(Key key, factor->keys()) {
std::cout << " " << gtsam::DefaultKeyFormatter(key);
}
} else {
std::cout << " NULL";
}
std::cout << " )" << std::endl;
}
/* ************************************************************************* */
void BatchFixedLagSmoother::PrintSymbolicFactor(const GaussianFactorOrdered::shared_ptr& factor, const OrderingOrdered& ordering) {
std::cout << "f(";
BOOST_FOREACH(Index index, factor->keys()) {
std::cout << " " << index << "[" << gtsam::DefaultKeyFormatter(ordering.key(index)) << "]";
}
std::cout << " )" << std::endl;
}
/* ************************************************************************* */
void BatchFixedLagSmoother::PrintSymbolicGraph(const NonlinearFactorGraph& graph, const std::string& label) {
std::cout << label << std::endl;
BOOST_FOREACH(const NonlinearFactor::shared_ptr& factor, graph) {
PrintSymbolicFactor(factor);
}
}
/* ************************************************************************* */
void BatchFixedLagSmoother::PrintSymbolicGraph(const GaussianFactorGraphOrdered& graph, const OrderingOrdered& ordering, const std::string& label) {
std::cout << label << std::endl;
BOOST_FOREACH(const GaussianFactorOrdered::shared_ptr& factor, graph) {
PrintSymbolicFactor(factor, ordering);
}
}
/* ************************************************************************* */
std::vector<Index> BatchFixedLagSmoother::EliminationForest::ComputeParents(const VariableIndexOrdered& structure) {
// Number of factors and variables
const size_t m = structure.nFactors();
const size_t n = structure.size();
static const Index none = std::numeric_limits<Index>::max();
// Allocate result parent vector and vector of last factor columns
std::vector<Index> parents(n, none);
std::vector<Index> prevCol(m, none);
// for column j \in 1 to n do
for (Index j = 0; j < n; j++) {
// for row i \in Struct[A*j] do
BOOST_FOREACH(const size_t i, structure[j]) {
if (prevCol[i] != none) {
Index k = prevCol[i];
// find root r of the current tree that contains k
Index r = k;
while (parents[r] != none)
r = parents[r];
if (r != j) parents[r] = j;
}
prevCol[i] = j;
}
}
return parents;
}
/* ************************************************************************* */
std::vector<BatchFixedLagSmoother::EliminationForest::shared_ptr> BatchFixedLagSmoother::EliminationForest::Create(const GaussianFactorGraphOrdered& factorGraph, const VariableIndexOrdered& structure) {
// Compute the tree structure
std::vector<Index> parents(ComputeParents(structure));
// Number of variables
const size_t n = structure.size();
static const Index none = std::numeric_limits<Index>::max();
// Create tree structure
std::vector<shared_ptr> trees(n);
for (Index k = 1; k <= n; k++) {
Index j = n - k; // Start at the last variable and loop down to 0
trees[j].reset(new EliminationForest(j)); // Create a new node on this variable
if (parents[j] != none) // If this node has a parent, add it to the parent's children
trees[parents[j]]->add(trees[j]);
}
// Hang factors in right places
BOOST_FOREACH(const GaussianFactorOrdered::shared_ptr& factor, factorGraph) {
if(factor && factor->size() > 0) {
Index j = *std::min_element(factor->begin(), factor->end());
if(j < structure.size())
trees[j]->add(factor);
}
}
return trees;
}
/* ************************************************************************* */
GaussianFactorOrdered::shared_ptr BatchFixedLagSmoother::EliminationForest::eliminateRecursive(GaussianFactorGraphOrdered::Eliminate function) {
// Create the list of factors to be eliminated, initially empty, and reserve space
GaussianFactorGraphOrdered factors;
factors.reserve(this->factors_.size() + this->subTrees_.size());
// Add all factors associated with the current node
factors.push_back(this->factors_.begin(), this->factors_.end());
// for all subtrees, eliminate into Bayes net and a separator factor, added to [factors]
BOOST_FOREACH(const shared_ptr& child, subTrees_)
factors.push_back(child->eliminateRecursive(function));
// Combine all factors (from this node and from subtrees) into a joint factor
GaussianFactorGraphOrdered::EliminationResult eliminated(function(factors, 1));
return eliminated.second;
}
/* ************************************************************************* */
void BatchFixedLagSmoother::EliminationForest::removeChildrenIndices(std::set<Index>& indices, const BatchFixedLagSmoother::EliminationForest::shared_ptr& tree) {
BOOST_FOREACH(const EliminationForest::shared_ptr& child, tree->children()) {
indices.erase(child->key());
removeChildrenIndices(indices, child);
}
}
/* ************************************************************************* */
} /// namespace gtsam
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