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Refactored iSAM2::update to reveal what is changed when

release/4.3a0
Frank Dellaert 2019-05-31 17:33:16 -04:00
parent 5f8a00fa53
commit 1a29ab5533
7 changed files with 915 additions and 937 deletions
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150
gtsam/nonlinear/ISAM2-impl.cpp
View File

@ -31,150 +31,6 @@ using namespace std;
namespace gtsam {
/* ************************************************************************* */
void ISAM2::Impl::AddFactorsStep1(const NonlinearFactorGraph& newFactors,
bool useUnusedSlots,
NonlinearFactorGraph* nonlinearFactors,
FactorIndices* newFactorIndices) {
newFactorIndices->resize(newFactors.size());
if (useUnusedSlots) {
size_t globalFactorIndex = 0;
for (size_t newFactorIndex = 0; newFactorIndex < newFactors.size();
++newFactorIndex) {
// Loop to find the next available factor slot
do {
// If we need to add more factors than we have room for, resize
// nonlinearFactors, filling the new slots with NULL factors. Otherwise,
// check if the current factor in nonlinearFactors is already used, and
// if so, increase globalFactorIndex. If the current factor in
// nonlinearFactors is unused, break out of the loop and use the current
// slot.
if (globalFactorIndex >= nonlinearFactors->size())
nonlinearFactors->resize(nonlinearFactors->size() +
newFactors.size() - newFactorIndex);
else if ((*nonlinearFactors)[globalFactorIndex])
++globalFactorIndex;
else
break;
} while (true);
// Use the current slot, updating nonlinearFactors and newFactorSlots.
(*nonlinearFactors)[globalFactorIndex] = newFactors[newFactorIndex];
(*newFactorIndices)[newFactorIndex] = globalFactorIndex;
}
} else {
// We're not looking for unused slots, so just add the factors at the end.
for (size_t i = 0; i < newFactors.size(); ++i)
(*newFactorIndices)[i] = i + nonlinearFactors->size();
nonlinearFactors->push_back(newFactors);
}
}
/* ************************************************************************* */
KeySet ISAM2::Impl::CheckRelinearizationFull(
const VectorValues& delta,
const ISAM2Params::RelinearizationThreshold& relinearizeThreshold) {
KeySet relinKeys;
if (const double* threshold = boost::get<double>(&relinearizeThreshold)) {
for (const VectorValues::KeyValuePair& key_delta : delta) {
double maxDelta = key_delta.second.lpNorm<Eigen::Infinity>();
if (maxDelta >= *threshold) relinKeys.insert(key_delta.first);
}
} else if (const FastMap<char, Vector>* thresholds =
boost::get<FastMap<char, Vector> >(&relinearizeThreshold)) {
for (const VectorValues::KeyValuePair& key_delta : delta) {
const Vector& threshold =
thresholds->find(Symbol(key_delta.first).chr())->second;
if (threshold.rows() != key_delta.second.rows())
throw std::invalid_argument(
"Relinearization threshold vector dimensionality for '" +
std::string(1, Symbol(key_delta.first).chr()) +
"' passed into iSAM2 parameters does not match actual variable "
"dimensionality.");
if ((key_delta.second.array().abs() > threshold.array()).any())
relinKeys.insert(key_delta.first);
}
}
return relinKeys;
}
/* ************************************************************************* */
static void CheckRelinearizationRecursiveDouble(
double threshold, const VectorValues& delta,
const ISAM2::sharedClique& clique, KeySet* relinKeys) {
// Check the current clique for relinearization
bool relinearize = false;
for (Key var : *clique->conditional()) {
double maxDelta = delta[var].lpNorm<Eigen::Infinity>();
if (maxDelta >= threshold) {
relinKeys->insert(var);
relinearize = true;
}
}
// If this node was relinearized, also check its children
if (relinearize) {
for (const ISAM2::sharedClique& child : clique->children) {
CheckRelinearizationRecursiveDouble(threshold, delta, child, relinKeys);
}
}
}
/* ************************************************************************* */
static void CheckRelinearizationRecursiveMap(
const FastMap<char, Vector>& thresholds, const VectorValues& delta,
const ISAM2::sharedClique& clique, KeySet* relinKeys) {
// Check the current clique for relinearization
bool relinearize = false;
for (Key var : *clique->conditional()) {
// Find the threshold for this variable type
const Vector& threshold = thresholds.find(Symbol(var).chr())->second;
const Vector& deltaVar = delta[var];
// Verify the threshold vector matches the actual variable size
if (threshold.rows() != deltaVar.rows())
throw std::invalid_argument(
"Relinearization threshold vector dimensionality for '" +
std::string(1, Symbol(var).chr()) +
"' passed into iSAM2 parameters does not match actual variable "
"dimensionality.");
// Check for relinearization
if ((deltaVar.array().abs() > threshold.array()).any()) {
relinKeys->insert(var);
relinearize = true;
}
}
// If this node was relinearized, also check its children
if (relinearize) {
for (const ISAM2::sharedClique& child : clique->children) {
CheckRelinearizationRecursiveMap(thresholds, delta, child, relinKeys);
}
}
}
/* ************************************************************************* */
KeySet ISAM2::Impl::CheckRelinearizationPartial(
const ISAM2::Roots& roots, const VectorValues& delta,
const ISAM2Params::RelinearizationThreshold& relinearizeThreshold) {
KeySet relinKeys;
for (const ISAM2::sharedClique& root : roots) {
if (relinearizeThreshold.type() == typeid(double))
CheckRelinearizationRecursiveDouble(
boost::get<double>(relinearizeThreshold), delta, root, &relinKeys);
else if (relinearizeThreshold.type() == typeid(FastMap<char, Vector>))
CheckRelinearizationRecursiveMap(
boost::get<FastMap<char, Vector> >(relinearizeThreshold), delta, root,
&relinKeys);
}
return relinKeys;
}
/* ************************************************************************* */
namespace internal {
inline static void optimizeInPlace(const ISAM2::sharedClique& clique,
@ -189,7 +45,7 @@ inline static void optimizeInPlace(const ISAM2::sharedClique& clique,
} // namespace internal
/* ************************************************************************* */
size_t ISAM2::Impl::UpdateGaussNewtonDelta(const ISAM2::Roots& roots,
size_t DeltaImpl::UpdateGaussNewtonDelta(const ISAM2::Roots& roots,
const KeySet& replacedKeys,
double wildfireThreshold,
VectorValues* delta) {
@ -272,7 +128,7 @@ void updateRgProd(const ISAM2::sharedClique& clique, const KeySet& replacedKeys,
} // namespace internal
/* ************************************************************************* */
size_t ISAM2::Impl::UpdateRgProd(const ISAM2::Roots& roots,
size_t DeltaImpl::UpdateRgProd(const ISAM2::Roots& roots,
const KeySet& replacedKeys,
const VectorValues& gradAtZero,
VectorValues* RgProd) {
@ -287,7 +143,7 @@ size_t ISAM2::Impl::UpdateRgProd(const ISAM2::Roots& roots,
}
/* ************************************************************************* */
VectorValues ISAM2::Impl::ComputeGradientSearch(const VectorValues& gradAtZero,
VectorValues DeltaImpl::ComputeGradientSearch(const VectorValues& gradAtZero,
const VectorValues& RgProd) {
// Compute gradient squared-magnitude
const double gradientSqNorm = gradAtZero.dot(gradAtZero);

843
gtsam/nonlinear/ISAM2-impl.h
View File

@ -11,18 +11,65 @@
/**
* @file ISAM2-impl.h
* @brief Incremental update functionality (ISAM2) for BayesTree, with fluid relinearization.
* @author Michael Kaess, Richard Roberts
* @brief Incremental update functionality (ISAM2) for BayesTree, with fluid
* relinearization.
* @author Michael Kaess, Richard Roberts, Frank Dellaert
*/
#pragma once
#include <gtsam/linear/GaussianBayesTree.h>
#include <gtsam/nonlinear/ISAM2.h>
#include <gtsam/nonlinear/ISAM2Result.h>
#include <gtsam/base/debug.h>
#include <gtsam/inference/JunctionTree-inst.h> // We need the inst file because we'll make a special JT templated on ISAM2
#include <gtsam/inference/Symbol.h>
#include <gtsam/inference/VariableIndex.h>
#include <gtsam/linear/GaussianBayesTree.h>
#include <gtsam/linear/GaussianEliminationTree.h>
#include <boost/range/adaptors.hpp>
#include <boost/range/algorithm/copy.hpp>
namespace br {
using namespace boost::range;
using namespace boost::adaptors;
} // namespace br
#include <algorithm>
#include <limits>
#include <string>
#include <utility>
namespace gtsam {
struct GTSAM_EXPORT ISAM2::Impl {
/* ************************************************************************* */
// Special BayesTree class that uses ISAM2 cliques - this is the result of
// reeliminating ISAM2 subtrees.
class ISAM2BayesTree : public ISAM2::Base {
public:
typedef ISAM2::Base Base;
typedef ISAM2BayesTree This;
typedef boost::shared_ptr<This> shared_ptr;
ISAM2BayesTree() {}
};
/* ************************************************************************* */
// Special JunctionTree class that produces ISAM2 BayesTree cliques, used for
// reeliminating ISAM2 subtrees.
class ISAM2JunctionTree
: public JunctionTree<ISAM2BayesTree, GaussianFactorGraph> {
public:
typedef JunctionTree<ISAM2BayesTree, GaussianFactorGraph> Base;
typedef ISAM2JunctionTree This;
typedef boost::shared_ptr<This> shared_ptr;
explicit ISAM2JunctionTree(const GaussianEliminationTree& eliminationTree)
: Base(eliminationTree) {}
};
/* ************************************************************************* */
struct GTSAM_EXPORT DeltaImpl {
struct GTSAM_EXPORT PartialSolveResult {
ISAM2::sharedClique bayesTree;
};
@ -34,57 +81,771 @@ struct GTSAM_EXPORT ISAM2::Impl {
boost::optional<FastMap<Key, int> > constrainedKeys;
};
/// Perform the first part of the bookkeeping updates for adding new factors. Adds them to the
/// complete list of nonlinear factors, and populates the list of new factor indices, both
/// optionally finding and reusing empty factor slots.
static void AddFactorsStep1(const NonlinearFactorGraph& newFactors, bool useUnusedSlots,
NonlinearFactorGraph* nonlinearFactors, FactorIndices* newFactorIndices);
/**
* Find the set of variables to be relinearized according to relinearizeThreshold.
* Any variables in the VectorValues delta whose vector magnitude is greater than
* or equal to relinearizeThreshold are returned.
* @param delta The linear delta to check against the threshold
* @param keyFormatter Formatter for printing nonlinear keys during debugging
* @return The set of variable indices in delta whose magnitude is greater than or
* equal to relinearizeThreshold
*/
static KeySet CheckRelinearizationFull(const VectorValues& delta,
const ISAM2Params::RelinearizationThreshold& relinearizeThreshold);
/**
* Find the set of variables to be relinearized according to relinearizeThreshold.
* This check is performed recursively, starting at the top of the tree. Once a
* variable in the tree does not need to be relinearized, no further checks in
* that branch are performed. This is an approximation of the Full version, designed
* to save time at the expense of accuracy.
* @param delta The linear delta to check against the threshold
* @param keyFormatter Formatter for printing nonlinear keys during debugging
* @return The set of variable indices in delta whose magnitude is greater than or
* equal to relinearizeThreshold
*/
static KeySet CheckRelinearizationPartial(const ISAM2::Roots& roots,
const VectorValues& delta, const ISAM2Params::RelinearizationThreshold& relinearizeThreshold);
/**
* Update the Newton's method step point, using wildfire
*/
static size_t UpdateGaussNewtonDelta(const ISAM2::Roots& roots,
const KeySet& replacedKeys, double wildfireThreshold, VectorValues* delta);
const KeySet& replacedKeys,
double wildfireThreshold,
VectorValues* delta);
/**
* Update the RgProd (R*g) incrementally taking into account which variables
* have been recalculated in \c replacedKeys. Only used in Dogleg.
*/
static size_t UpdateRgProd(const ISAM2::Roots& roots, const KeySet& replacedKeys,
const VectorValues& gradAtZero, VectorValues* RgProd);
static size_t UpdateRgProd(const ISAM2::Roots& roots,
const KeySet& replacedKeys,
const VectorValues& gradAtZero,
VectorValues* RgProd);
/**
* Compute the gradient-search point. Only used in Dogleg.
*/
static VectorValues ComputeGradientSearch(const VectorValues& gradAtZero,
const VectorValues& RgProd);
};
}
/* ************************************************************************* */
/**
* Implementation functions for update method
* All of the methods below have clear inputs and outputs, even if not
* functional: iSAM2 is inherintly imperative.
*/
struct GTSAM_EXPORT UpdateImpl {
const ISAM2Params& params_;
const ISAM2UpdateParams& updateParams_;
UpdateImpl(const ISAM2Params& params, const ISAM2UpdateParams& updateParams)
: params_(params), updateParams_(updateParams) {}
/// Perform the first part of the bookkeeping updates for adding new factors.
/// Adds them to the complete list of nonlinear factors, and populates the
/// list of new factor indices, both optionally finding and reusing empty
/// factor slots.
static void AddFactorsStep1(const NonlinearFactorGraph& newFactors,
bool useUnusedSlots,
NonlinearFactorGraph* nonlinearFactors,
FactorIndices* newFactorIndices) {
newFactorIndices->resize(newFactors.size());
if (useUnusedSlots) {
size_t globalFactorIndex = 0;
for (size_t newFactorIndex = 0; newFactorIndex < newFactors.size();
++newFactorIndex) {
// Loop to find the next available factor slot
do {
// If we need to add more factors than we have room for, resize
// nonlinearFactors, filling the new slots with NULL factors.
// Otherwise, check if the current factor in nonlinearFactors is
// already used, and if so, increase globalFactorIndex. If the
// current factor in nonlinearFactors is unused, break out of the loop
// and use the current slot.
if (globalFactorIndex >= nonlinearFactors->size())
nonlinearFactors->resize(nonlinearFactors->size() +
newFactors.size() - newFactorIndex);
else if ((*nonlinearFactors)[globalFactorIndex])
++globalFactorIndex;
else
break;
} while (true);
// Use the current slot, updating nonlinearFactors and newFactorSlots.
(*nonlinearFactors)[globalFactorIndex] = newFactors[newFactorIndex];
(*newFactorIndices)[newFactorIndex] = globalFactorIndex;
}
} else {
// We're not looking for unused slots, so just add the factors at the end.
for (size_t i = 0; i < newFactors.size(); ++i)
(*newFactorIndices)[i] = i + nonlinearFactors->size();
nonlinearFactors->push_back(newFactors);
}
}
// 1. Add any new factors \Factors:=\Factors\cup\Factors'.
void pushBackFactors(const NonlinearFactorGraph& newFactors,
NonlinearFactorGraph* nonlinearFactors,
GaussianFactorGraph* linearFactors,
VariableIndex* variableIndex,
ISAM2Result* result) const {
gttic(pushBackFactors);
const bool debug = ISDEBUG("ISAM2 update");
const bool verbose = ISDEBUG("ISAM2 update verbose");
// Add the new factor indices to the result struct
if (debug || verbose) newFactors.print("The new factors are: ");
AddFactorsStep1(newFactors, params_.findUnusedFactorSlots, nonlinearFactors,
&result->newFactorsIndices);
// Remove the removed factors
NonlinearFactorGraph removedFactors;
removedFactors.reserve(updateParams_.removeFactorIndices.size());
for (const auto index : updateParams_.removeFactorIndices) {
removedFactors.push_back(nonlinearFactors->at(index));
nonlinearFactors->remove(index);
if (params_.cacheLinearizedFactors) linearFactors->remove(index);
}
// Remove removed factors from the variable index so we do not attempt to
// relinearize them
variableIndex->remove(updateParams_.removeFactorIndices.begin(),
updateParams_.removeFactorIndices.end(),
removedFactors);
result->keysWithRemovedFactors = removedFactors.keys();
// Compute unused keys and indices
// Get keys from removed factors and new factors, and compute unused keys,
// i.e., keys that are empty now and do not appear in the new factors.
KeySet removedAndEmpty;
for (Key key : result->keysWithRemovedFactors) {
if (variableIndex->empty(key))
removedAndEmpty.insert(removedAndEmpty.end(), key);
}
KeySet newFactorSymbKeys = newFactors.keys();
std::set_difference(
removedAndEmpty.begin(), removedAndEmpty.end(),
newFactorSymbKeys.begin(), newFactorSymbKeys.end(),
std::inserter(result->unusedKeys, result->unusedKeys.end()));
// Get indices for unused keys
for (Key key : result->unusedKeys) {
result->unusedIndices.insert(result->unusedIndices.end(), key);
}
}
// 3. Mark linear update
void gatherInvolvedKeys(const NonlinearFactorGraph& newFactors,
const NonlinearFactorGraph& nonlinearFactors,
ISAM2Result* result) const {
gttic(gatherInvolvedKeys);
result->markedKeys = newFactors.keys(); // Get keys from new factors
// Also mark keys involved in removed factors
result->markedKeys.insert(result->keysWithRemovedFactors.begin(),
result->keysWithRemovedFactors.end());
// Also mark any provided extra re-eliminate keys
if (updateParams_.extraReelimKeys) {
for (Key key : *updateParams_.extraReelimKeys) {
result->markedKeys.insert(key);
}
}
// Also, keys that were not observed in existing factors, but whose affected
// keys have been extended now (e.g. smart factors)
if (updateParams_.newAffectedKeys) {
for (const auto& factorAddedKeys : *updateParams_.newAffectedKeys) {
const auto factorIdx = factorAddedKeys.first;
const auto& affectedKeys = nonlinearFactors.at(factorIdx)->keys();
result->markedKeys.insert(affectedKeys.begin(), affectedKeys.end());
}
}
// Observed keys for detailed results
if (params_.enableDetailedResults) {
for (Key key : result->markedKeys) {
result->detail->variableStatus[key].isObserved = true;
}
}
for (Key index : result->markedKeys) {
// Only add if not unused
if (result->unusedIndices.find(index) == result->unusedIndices.end())
// Make a copy of these, as we'll soon add to them
result->observedKeys.push_back(index);
}
}
static void CheckRelinearizationRecursiveMap(
const FastMap<char, Vector>& thresholds, const VectorValues& delta,
const ISAM2::sharedClique& clique, KeySet* relinKeys) {
// Check the current clique for relinearization
bool relinearize = false;
for (Key var : *clique->conditional()) {
// Find the threshold for this variable type
const Vector& threshold = thresholds.find(Symbol(var).chr())->second;
const Vector& deltaVar = delta[var];
// Verify the threshold vector matches the actual variable size
if (threshold.rows() != deltaVar.rows())
throw std::invalid_argument(
"Relinearization threshold vector dimensionality for '" +
std::string(1, Symbol(var).chr()) +
"' passed into iSAM2 parameters does not match actual variable "
"dimensionality.");
// Check for relinearization
if ((deltaVar.array().abs() > threshold.array()).any()) {
relinKeys->insert(var);
relinearize = true;
}
}
// If this node was relinearized, also check its children
if (relinearize) {
for (const ISAM2::sharedClique& child : clique->children) {
CheckRelinearizationRecursiveMap(thresholds, delta, child, relinKeys);
}
}
}
static void CheckRelinearizationRecursiveDouble(
double threshold, const VectorValues& delta,
const ISAM2::sharedClique& clique, KeySet* relinKeys) {
// Check the current clique for relinearization
bool relinearize = false;
for (Key var : *clique->conditional()) {
double maxDelta = delta[var].lpNorm<Eigen::Infinity>();
if (maxDelta >= threshold) {
relinKeys->insert(var);
relinearize = true;
}
}
// If this node was relinearized, also check its children
if (relinearize) {
for (const ISAM2::sharedClique& child : clique->children) {
CheckRelinearizationRecursiveDouble(threshold, delta, child, relinKeys);
}
}
}
/**
* Find the set of variables to be relinearized according to
* relinearizeThreshold. This check is performed recursively, starting at the
* top of the tree. Once a variable in the tree does not need to be
* relinearized, no further checks in that branch are performed. This is an
* approximation of the Full version, designed to save time at the expense of
* accuracy.
* @param delta The linear delta to check against the threshold
* @param keyFormatter Formatter for printing nonlinear keys during debugging
* @return The set of variable indices in delta whose magnitude is greater
* than or equal to relinearizeThreshold
*/
static KeySet CheckRelinearizationPartial(
const ISAM2::Roots& roots, const VectorValues& delta,
const ISAM2Params::RelinearizationThreshold& relinearizeThreshold) {
KeySet relinKeys;
for (const ISAM2::sharedClique& root : roots) {
if (relinearizeThreshold.type() == typeid(double))
CheckRelinearizationRecursiveDouble(
boost::get<double>(relinearizeThreshold), delta, root, &relinKeys);
else if (relinearizeThreshold.type() == typeid(FastMap<char, Vector>))
CheckRelinearizationRecursiveMap(
boost::get<FastMap<char, Vector> >(relinearizeThreshold), delta,
root, &relinKeys);
}
return relinKeys;
}
/**
* Find the set of variables to be relinearized according to
* relinearizeThreshold. Any variables in the VectorValues delta whose vector
* magnitude is greater than or equal to relinearizeThreshold are returned.
* @param delta The linear delta to check against the threshold
* @param keyFormatter Formatter for printing nonlinear keys during debugging
* @return The set of variable indices in delta whose magnitude is greater
* than or equal to relinearizeThreshold
*/
static KeySet CheckRelinearizationFull(
const VectorValues& delta,
const ISAM2Params::RelinearizationThreshold& relinearizeThreshold) {
KeySet relinKeys;
if (const double* threshold = boost::get<double>(&relinearizeThreshold)) {
for (const VectorValues::KeyValuePair& key_delta : delta) {
double maxDelta = key_delta.second.lpNorm<Eigen::Infinity>();
if (maxDelta >= *threshold) relinKeys.insert(key_delta.first);
}
} else if (const FastMap<char, Vector>* thresholds =
boost::get<FastMap<char, Vector> >(&relinearizeThreshold)) {
for (const VectorValues::KeyValuePair& key_delta : delta) {
const Vector& threshold =
thresholds->find(Symbol(key_delta.first).chr())->second;
if (threshold.rows() != key_delta.second.rows())
throw std::invalid_argument(
"Relinearization threshold vector dimensionality for '" +
std::string(1, Symbol(key_delta.first).chr()) +
"' passed into iSAM2 parameters does not match actual variable "
"dimensionality.");
if ((key_delta.second.array().abs() > threshold.array()).any())
relinKeys.insert(key_delta.first);
}
}
return relinKeys;
}
/**
* Apply expmap to the given values, but only for indices appearing in
* \c mask. Values are expmapped in-place.
* \param mask Mask on linear indices, only \c true entries are expmapped
*/
void expmapMasked(const VectorValues& delta, const KeySet& mask,
Values* theta) const {
assert(theta->size() == delta.size());
Values::iterator key_value;
VectorValues::const_iterator key_delta;
#ifdef GTSAM_USE_TBB
for (key_value = theta->begin(); key_value != theta->end(); ++key_value) {
key_delta = delta.find(key_value->key);
#else
for (key_value = theta->begin(), key_delta = delta.begin();
key_value != theta->end(); ++key_value, ++key_delta) {
assert(key_value->key == key_delta->first);
#endif
Key var = key_value->key;
assert(static_cast<size_t>(delta[var].size()) == key_value->value.dim());
assert(delta[var].allFinite());
if (mask.exists(var)) {
Value* retracted = key_value->value.retract_(delta[var]);
key_value->value = *retracted;
retracted->deallocate_();
}
}
}
KeySet relinearize(const ISAM2::Roots& roots, const VectorValues& delta,
const KeySet& fixedVariables, Values* theta,
ISAM2Result* result) const {
KeySet relinKeys;
gttic(gather_relinearize_keys);
// 4. Mark keys in \Delta above threshold \beta:
// J=\{\Delta_{j}\in\Delta|\Delta_{j}\geq\beta\}.
if (params_.enablePartialRelinearizationCheck)
relinKeys = CheckRelinearizationPartial(roots, delta,
params_.relinearizeThreshold);
else
relinKeys = CheckRelinearizationFull(delta, params_.relinearizeThreshold);
if (updateParams_.forceFullSolve)
relinKeys =
CheckRelinearizationFull(delta, 0.0); // This is used for debugging
// Remove from relinKeys any keys whose linearization points are fixed
for (Key key : fixedVariables) {
relinKeys.erase(key);
}
if (updateParams_.noRelinKeys) {
for (Key key : *updateParams_.noRelinKeys) {
relinKeys.erase(key);
}
}
// Above relin threshold keys for detailed results
if (params_.enableDetailedResults) {
for (Key key : relinKeys) {
result->detail->variableStatus[key].isAboveRelinThreshold = true;
result->detail->variableStatus[key].isRelinearized = true;
}
}
// Add the variables being relinearized to the marked keys
KeySet markedRelinMask;
for (const Key key : relinKeys) markedRelinMask.insert(key);
result->markedKeys.insert(relinKeys.begin(), relinKeys.end());
gttoc(gather_relinearize_keys);
gttic(fluid_find_all);
// 5. Mark all cliques that involve marked variables \Theta_{J} and all
// their ancestors.
if (!relinKeys.empty()) {
for (const auto& root : roots)
// add other cliques that have the marked ones in the separator
root->findAll(markedRelinMask, &result->markedKeys);
// Relin involved keys for detailed results
if (params_.enableDetailedResults) {
KeySet involvedRelinKeys;
for (const auto& root : roots)
root->findAll(markedRelinMask, &involvedRelinKeys);
for (Key key : involvedRelinKeys) {
if (!result->detail->variableStatus[key].isAboveRelinThreshold) {
result->detail->variableStatus[key].isRelinearizeInvolved = true;
result->detail->variableStatus[key].isRelinearized = true;
}
}
}
}
gttoc(fluid_find_all);
gttic(expmap);
// 6. Update linearization point for marked variables:
// \Theta_{J}:=\Theta_{J}+\Delta_{J}.
if (!relinKeys.empty()) expmapMasked(delta, markedRelinMask, theta);
gttoc(expmap);
result->variablesRelinearized = result->markedKeys.size();
return relinKeys;
}
// 7. Linearize new factors
void linearizeNewFactors(const NonlinearFactorGraph& newFactors,
const Values& theta, size_t numNonlinearFactors,
const FactorIndices& newFactorsIndices,
GaussianFactorGraph* linearFactors) const {
gttic(linearizeNewFactors);
auto linearized = newFactors.linearize(theta);
if (params_.findUnusedFactorSlots) {
linearFactors->resize(numNonlinearFactors);
for (size_t i = 0; i < newFactors.size(); ++i)
(*linearFactors)[newFactorsIndices[i]] = (*linearized)[i];
} else {
linearFactors->push_back(*linearized);
}
assert(linearFactors->size() == numNonlinearFactors);
}
void augmentVariableIndex(const NonlinearFactorGraph& newFactors,
const FactorIndices& newFactorsIndices,
VariableIndex* variableIndex) const {
gttic(augmentVariableIndex);
// Augment the variable index with the new factors
if (params_.findUnusedFactorSlots)
variableIndex->augment(newFactors, newFactorsIndices);
else
variableIndex->augment(newFactors);
// Augment it with existing factors which now affect to more variables:
if (updateParams_.newAffectedKeys) {
for (const auto& factorAddedKeys : *updateParams_.newAffectedKeys) {
const auto factorIdx = factorAddedKeys.first;
variableIndex->augmentExistingFactor(factorIdx, factorAddedKeys.second);
}
}
}
void logRecalculateKeys(const ISAM2Result& result) const {
const bool debug = ISDEBUG("ISAM2 recalculate");
if (debug) {
std::cout << "markedKeys: ";
for (const Key key : result.markedKeys) {
std::cout << key << " ";
}
std::cout << std::endl;
std::cout << "observedKeys: ";
for (const Key key : result.observedKeys) {
std::cout << key << " ";
}
std::cout << std::endl;
}
}
FactorIndexSet getAffectedFactors(const KeyList& keys,
const VariableIndex& variableIndex) const {
FactorIndexSet indices;
for (const Key key : keys) {
const FactorIndices& factors(variableIndex[key]);
indices.insert(factors.begin(), factors.end());
}
return indices;
}
// find intermediate (linearized) factors from cache that are passed into the
// affected area
GaussianFactorGraph getCachedBoundaryFactors(
const ISAM2::Cliques& orphans) const {
GaussianFactorGraph cachedBoundary;
for (const auto& orphan : orphans) {
// retrieve the cached factor and add to boundary
cachedBoundary.push_back(orphan->cachedFactor());
}
return cachedBoundary;
}
// retrieve all factors that ONLY contain the affected variables
// (note that the remaining stuff is summarized in the cached factors)
GaussianFactorGraph relinearizeAffectedFactors(
const FastList<Key>& affectedKeys, const KeySet& relinKeys,
const NonlinearFactorGraph& nonlinearFactors,
const VariableIndex& variableIndex, const Values& theta,
GaussianFactorGraph* linearFactors) const {
gttic(getAffectedFactors);
FactorIndexSet candidates = getAffectedFactors(affectedKeys, variableIndex);
gttoc(getAffectedFactors);
gttic(affectedKeysSet);
// for fast lookup below
KeySet affectedKeysSet;
affectedKeysSet.insert(affectedKeys.begin(), affectedKeys.end());
gttoc(affectedKeysSet);
gttic(check_candidates_and_linearize);
GaussianFactorGraph linearized;
for (const FactorIndex idx : candidates) {
bool inside = true;
bool useCachedLinear = params_.cacheLinearizedFactors;
for (Key key : nonlinearFactors[idx]->keys()) {
if (affectedKeysSet.find(key) == affectedKeysSet.end()) {
inside = false;
break;
}
if (useCachedLinear && relinKeys.find(key) != relinKeys.end())
useCachedLinear = false;
}
if (inside) {
if (useCachedLinear) {
#ifdef GTSAM_EXTRA_CONSISTENCY_CHECKS
assert((*linearFactors)[idx]);
assert((*linearFactors)[idx]->keys() ==
nonlinearFactors[idx]->keys());
#endif
linearized.push_back((*linearFactors)[idx]);
} else {
auto linearFactor = nonlinearFactors[idx]->linearize(theta);
linearized.push_back(linearFactor);
if (params_.cacheLinearizedFactors) {
#ifdef GTSAM_EXTRA_CONSISTENCY_CHECKS
assert((*linearFactors)[idx]->keys() == linearFactor->keys());
#endif
(*linearFactors)[idx] = linearFactor;
}
}
}
}
gttoc(check_candidates_and_linearize);
return linearized;
}
KeySet recalculate(const Values& theta, const VariableIndex& variableIndex,
const NonlinearFactorGraph& nonlinearFactors,
const GaussianBayesNet& affectedBayesNet,
const ISAM2::Cliques& orphans, const KeySet& relinKeys,
GaussianFactorGraph* linearFactors, ISAM2::Roots* roots,
ISAM2::Nodes* nodes, ISAM2Result* result) const {
gttic(recalculate);
logRecalculateKeys(*result);
// FactorGraph<GaussianFactor> factors(affectedBayesNet);
// bug was here: we cannot reuse the original factors, because then the
// cached factors get messed up [all the necessary data is actually
// contained in the affectedBayesNet, including what was passed in from the
// boundaries,
// so this would be correct; however, in the process we also generate new
// cached_ entries that will be wrong (ie. they don't contain what would be
// passed up at a certain point if batch elimination was done, but that's
// what we need); we could choose not to update cached_ from here, but then
// the new information (and potentially different variable ordering) is not
// reflected in the cached_ values which again will be wrong]
// so instead we have to retrieve the original linearized factors AND add
// the cached factors from the boundary
// BEGIN OF COPIED CODE
// ordering provides all keys in conditionals, there cannot be others
// because path to root included
gttic(affectedKeys);
FastList<Key> affectedKeys;
for (const auto& conditional : affectedBayesNet)
affectedKeys.insert(affectedKeys.end(), conditional->beginFrontals(),
conditional->endFrontals());
gttoc(affectedKeys);
KeySet affectedKeysSet; // Will return this result
static const double kBatchThreshold = 0.65;
if (affectedKeys.size() >= theta.size() * kBatchThreshold) {
// Do a batch step - reorder and relinearize all variables
gttic(batch);
gttic(add_keys);
br::copy(variableIndex | br::map_keys,
std::inserter(affectedKeysSet, affectedKeysSet.end()));
// Removed unused keys:
VariableIndex affectedFactorsVarIndex = variableIndex;
affectedFactorsVarIndex.removeUnusedVariables(
result->unusedIndices.begin(), result->unusedIndices.end());
for (const Key key : result->unusedIndices) {
affectedKeysSet.erase(key);
}
gttoc(add_keys);
gttic(ordering);
Ordering order;
if (updateParams_.constrainedKeys) {
order = Ordering::ColamdConstrained(affectedFactorsVarIndex,
*updateParams_.constrainedKeys);
} else {
if (theta.size() > result->observedKeys.size()) {
// Only if some variables are unconstrained
FastMap<Key, int> constraintGroups;
for (Key var : result->observedKeys) constraintGroups[var] = 1;
order = Ordering::ColamdConstrained(affectedFactorsVarIndex,
constraintGroups);
} else {
order = Ordering::Colamd(affectedFactorsVarIndex);
}
}
gttoc(ordering);
gttic(linearize);
GaussianFactorGraph linearized = *nonlinearFactors.linearize(theta);
if (params_.cacheLinearizedFactors) *linearFactors = linearized;
gttoc(linearize);
gttic(eliminate);
ISAM2BayesTree::shared_ptr bayesTree =
ISAM2JunctionTree(GaussianEliminationTree(
linearized, affectedFactorsVarIndex, order))
.eliminate(params_.getEliminationFunction())
.first;
gttoc(eliminate);
gttic(insert);
roots->clear();
roots->insert(roots->end(), bayesTree->roots().begin(),
bayesTree->roots().end());
nodes->clear();
nodes->insert(bayesTree->nodes().begin(), bayesTree->nodes().end());
gttoc(insert);
result->variablesReeliminated = affectedKeysSet.size();
result->factorsRecalculated = nonlinearFactors.size();
// Reeliminated keys for detailed results
if (params_.enableDetailedResults) {
for (Key key : theta.keys()) {
result->detail->variableStatus[key].isReeliminated = true;
}
}
gttoc(batch);
} else {
gttic(incremental);
const bool debug = ISDEBUG("ISAM2 recalculate");
// 2. Add the new factors \Factors' into the resulting factor graph
FastList<Key> affectedAndNewKeys;
affectedAndNewKeys.insert(affectedAndNewKeys.end(), affectedKeys.begin(),
affectedKeys.end());
affectedAndNewKeys.insert(affectedAndNewKeys.end(),
result->observedKeys.begin(),
result->observedKeys.end());
gttic(relinearizeAffected);
GaussianFactorGraph factors = relinearizeAffectedFactors(
affectedAndNewKeys, relinKeys, nonlinearFactors, variableIndex, theta,
linearFactors);
gttoc(relinearizeAffected);
if (debug) {
factors.print("Relinearized factors: ");
std::cout << "Affected keys: ";
for (const Key key : affectedKeys) {
std::cout << key << " ";
}
std::cout << std::endl;
}
// Reeliminated keys for detailed results
if (params_.enableDetailedResults) {
for (Key key : affectedAndNewKeys) {
result->detail->variableStatus[key].isReeliminated = true;
}
}
result->variablesReeliminated = affectedAndNewKeys.size();
result->factorsRecalculated = factors.size();
gttic(cached);
// add the cached intermediate results from the boundary of the orphans
// ...
GaussianFactorGraph cachedBoundary = getCachedBoundaryFactors(orphans);
if (debug) cachedBoundary.print("Boundary factors: ");
factors.push_back(cachedBoundary);
gttoc(cached);
gttic(orphans);
// Add the orphaned subtrees
for (const auto& orphan : orphans)
factors +=
boost::make_shared<BayesTreeOrphanWrapper<ISAM2::Clique> >(orphan);
gttoc(orphans);
// END OF COPIED CODE
// 3. Re-order and eliminate the factor graph into a Bayes net (Algorithm
// [alg:eliminate]), and re-assemble into a new Bayes tree (Algorithm
// [alg:BayesTree])
gttic(reorder_and_eliminate);
gttic(list_to_set);
// create a partial reordering for the new and contaminated factors
// result->markedKeys are passed in: those variables will be forced to the
// end in the ordering
affectedKeysSet.insert(result->markedKeys.begin(),
result->markedKeys.end());
affectedKeysSet.insert(affectedKeys.begin(), affectedKeys.end());
gttoc(list_to_set);
VariableIndex affectedFactorsVarIndex(factors);
gttic(ordering_constraints);
// Create ordering constraints
FastMap<Key, int> constraintGroups;
if (updateParams_.constrainedKeys) {
constraintGroups = *updateParams_.constrainedKeys;
} else {
constraintGroups = FastMap<Key, int>();
const int group =
result->observedKeys.size() < affectedFactorsVarIndex.size() ? 1
: 0;
for (Key var : result->observedKeys)
constraintGroups.insert(std::make_pair(var, group));
}
// Remove unaffected keys from the constraints
for (FastMap<Key, int>::iterator iter = constraintGroups.begin();
iter != constraintGroups.end();
/*Incremented in loop ++iter*/) {
if (result->unusedIndices.exists(iter->first) ||
!affectedKeysSet.exists(iter->first))
constraintGroups.erase(iter++);
else
++iter;
}
gttoc(ordering_constraints);
// Generate ordering
gttic(Ordering);
Ordering ordering = Ordering::ColamdConstrained(affectedFactorsVarIndex,
constraintGroups);
gttoc(Ordering);
ISAM2BayesTree::shared_ptr bayesTree =
ISAM2JunctionTree(GaussianEliminationTree(
factors, affectedFactorsVarIndex, ordering))
.eliminate(params_.getEliminationFunction())
.first;
gttoc(reorder_and_eliminate);
gttic(reassemble);
roots->insert(roots->end(), bayesTree->roots().begin(),
bayesTree->roots().end());
nodes->insert(bayesTree->nodes().begin(), bayesTree->nodes().end());
gttoc(reassemble);
// 4. The orphans have already been inserted during elimination
gttoc(incremental);
}
// Root clique variables for detailed results
if (params_.enableDetailedResults) {
for (const auto& root : *roots)
for (Key var : *root->conditional())
result->detail->variableStatus[var].inRootClique = true;
}
return affectedKeysSet;
}
};
/* ************************************************************************* */
} // namespace gtsam

752
gtsam/nonlinear/ISAM2.cpp
View File

@ -16,26 +16,16 @@
* @author Michael Kaess, Richard Roberts, Frank Dellaert
*/
#include <gtsam/nonlinear/ISAM2-impl.h>
#include <gtsam/nonlinear/ISAM2.h>
#include <gtsam/nonlinear/ISAM2Result.h>
#include <gtsam/base/debug.h>
#include <gtsam/base/timing.h>
#include <gtsam/inference/BayesTree-inst.h>
#include <gtsam/inference/JunctionTree-inst.h> // We need the inst file because we'll make a special JT templated on ISAM2
#include <gtsam/linear/GaussianEliminationTree.h>
#include <gtsam/nonlinear/LinearContainerFactor.h>
#include <boost/range/adaptors.hpp>
#include <boost/range/algorithm/copy.hpp>
namespace br {
using namespace boost::range;
using namespace boost::adaptors;
} // namespace br
#include <algorithm>
#include <limits>
#include <map>
#include <utility>
using namespace std;
@ -44,35 +34,6 @@ namespace gtsam {
// Instantiate base class
template class BayesTree<ISAM2Clique>;
static const bool kDisableReordering = false;
static const double kBatchThreshold = 0.65;
/* ************************************************************************* */
// Special BayesTree class that uses ISAM2 cliques - this is the result of
// reeliminating ISAM2 subtrees.
class ISAM2BayesTree : public ISAM2::Base {
public:
typedef ISAM2::Base Base;
typedef ISAM2BayesTree This;
typedef boost::shared_ptr<This> shared_ptr;
ISAM2BayesTree() {}
};
/* ************************************************************************* */
// Special JunctionTree class that produces ISAM2 BayesTree cliques, used for
// reeliminating ISAM2 subtrees.
class ISAM2JunctionTree
: public JunctionTree<ISAM2BayesTree, GaussianFactorGraph> {
public:
typedef JunctionTree<ISAM2BayesTree, GaussianFactorGraph> Base;
typedef ISAM2JunctionTree This;
typedef boost::shared_ptr<This> shared_ptr;
explicit ISAM2JunctionTree(const GaussianEliminationTree& eliminationTree)
: Base(eliminationTree) {}
};
/* ************************************************************************* */
ISAM2::ISAM2(const ISAM2Params& params) : params_(params), update_count_(0) {
if (params_.optimizationParams.type() == typeid(ISAM2DoglegParams))
@ -96,392 +57,11 @@ bool ISAM2::equals(const ISAM2& other, double tol) const {
}
/* ************************************************************************* */
FactorIndexSet ISAM2::getAffectedFactors(const KeyList& keys) const {
static const bool debug = false;
if (debug) cout << "Getting affected factors for ";
if (debug) {
for (const Key key : keys) {
cout << key << " ";
}
}
if (debug) cout << endl;
FactorIndexSet indices;
for (const Key key : keys) {
const VariableIndex::Factors& factors(variableIndex_[key]);
indices.insert(factors.begin(), factors.end());
}
if (debug) cout << "Affected factors are: ";
if (debug) {
for (const auto index : indices) {
cout << index << " ";
}
}
if (debug) cout << endl;
return indices;
}
/* ************************************************************************* */
// retrieve all factors that ONLY contain the affected variables
// (note that the remaining stuff is summarized in the cached factors)
GaussianFactorGraph::shared_ptr ISAM2::relinearizeAffectedFactors(
const FastList<Key>& affectedKeys, const KeySet& relinKeys) const {
gttic(getAffectedFactors);
FactorIndexSet candidates = getAffectedFactors(affectedKeys);
gttoc(getAffectedFactors);
gttic(affectedKeysSet);
// for fast lookup below
KeySet affectedKeysSet;
affectedKeysSet.insert(affectedKeys.begin(), affectedKeys.end());
gttoc(affectedKeysSet);
gttic(check_candidates_and_linearize);
auto linearized = boost::make_shared<GaussianFactorGraph>();
for (const FactorIndex idx : candidates) {
bool inside = true;
bool useCachedLinear = params_.cacheLinearizedFactors;
for (Key key : nonlinearFactors_[idx]->keys()) {
if (affectedKeysSet.find(key) == affectedKeysSet.end()) {
inside = false;
break;
}
if (useCachedLinear && relinKeys.find(key) != relinKeys.end())
useCachedLinear = false;
}
if (inside) {
if (useCachedLinear) {
#ifdef GTSAM_EXTRA_CONSISTENCY_CHECKS
assert(linearFactors_[idx]);
assert(linearFactors_[idx]->keys() == nonlinearFactors_[idx]->keys());
#endif
linearized->push_back(linearFactors_[idx]);
} else {
auto linearFactor = nonlinearFactors_[idx]->linearize(theta_);
linearized->push_back(linearFactor);
if (params_.cacheLinearizedFactors) {
#ifdef GTSAM_EXTRA_CONSISTENCY_CHECKS
assert(linearFactors_[idx]->keys() == linearFactor->keys());
#endif
linearFactors_[idx] = linearFactor;
}
}
}
}
gttoc(check_candidates_and_linearize);
return linearized;
}
/* ************************************************************************* */
// find intermediate (linearized) factors from cache that are passed into the
// affected area
GaussianFactorGraph ISAM2::getCachedBoundaryFactors(const Cliques& orphans) {
GaussianFactorGraph cachedBoundary;
for (sharedClique orphan : orphans) {
// retrieve the cached factor and add to boundary
cachedBoundary.push_back(orphan->cachedFactor());
}
return cachedBoundary;
}
/* ************************************************************************* */
boost::shared_ptr<KeySet> ISAM2::recalculate(
const KeySet& markedKeys, const KeySet& relinKeys,
const KeyVector& observedKeys, const KeySet& unusedIndices,
const boost::optional<FastMap<Key, int> >& constrainKeys,
ISAM2Result* result) {
// TODO(dellaert): new factors are linearized twice,
// the newFactors passed in are not used.
const bool debug = ISDEBUG("ISAM2 recalculate");
// Input: BayesTree(this), newFactors
// figures for paper, disable for timing
#ifdef PRINT_STATS
static int counter = 0;
int maxClique = 0;
double avgClique = 0;
int numCliques = 0;
int nnzR = 0;
if (counter > 0) { // cannot call on empty tree
GaussianISAM2_P::CliqueData cdata = this->getCliqueData();
GaussianISAM2_P::CliqueStats cstats = cdata.getStats();
maxClique = cstats.maxCONDITIONALSize;
avgClique = cstats.avgCONDITIONALSize;
numCliques = cdata.conditionalSizes.size();
nnzR = calculate_nnz(this->root());
}
counter++;
#endif
if (debug) {
cout << "markedKeys: ";
for (const Key key : markedKeys) {
cout << key << " ";
}
cout << endl;
cout << "observedKeys: ";
for (const Key key : observedKeys) {
cout << key << " ";
}
cout << endl;
}
// 1. Remove top of Bayes tree and convert to a factor graph:
// (a) For each affected variable, remove the corresponding clique and all
// parents up to the root. (b) Store orphaned sub-trees \BayesTree_{O} of
// removed cliques.
gttic(removetop);
Cliques orphans;
GaussianBayesNet affectedBayesNet;
this->removeTop(KeyVector(markedKeys.begin(), markedKeys.end()),
affectedBayesNet, orphans);
gttoc(removetop);
// FactorGraph<GaussianFactor> factors(affectedBayesNet);
// bug was here: we cannot reuse the original factors, because then the cached
// factors get messed up [all the necessary data is actually contained in the
// affectedBayesNet, including what was passed in from the boundaries,
// so this would be correct; however, in the process we also generate new
// cached_ entries that will be wrong (ie. they don't contain what would be
// passed up at a certain point if batch elimination was done, but that's
// what we need); we could choose not to update cached_ from here, but then
// the new information (and potentially different variable ordering) is not
// reflected in the cached_ values which again will be wrong]
// so instead we have to retrieve the original linearized factors AND add the
// cached factors from the boundary
// BEGIN OF COPIED CODE
// ordering provides all keys in conditionals, there cannot be others because
// path to root included
gttic(affectedKeys);
FastList<Key> affectedKeys;
for (const auto& conditional : affectedBayesNet)
affectedKeys.insert(affectedKeys.end(), conditional->beginFrontals(),
conditional->endFrontals());
gttoc(affectedKeys);
boost::shared_ptr<KeySet> affectedKeysSet(
new KeySet()); // Will return this result
if (affectedKeys.size() >= theta_.size() * kBatchThreshold) {
// Do a batch step - reorder and relinearize all variables
gttic(batch);
gttic(add_keys);
br::copy(variableIndex_ | br::map_keys,
std::inserter(*affectedKeysSet, affectedKeysSet->end()));
// Removed unused keys:
VariableIndex affectedFactorsVarIndex = variableIndex_;
affectedFactorsVarIndex.removeUnusedVariables(unusedIndices.begin(),
unusedIndices.end());
for (const Key key : unusedIndices) {
affectedKeysSet->erase(key);
}
gttoc(add_keys);
gttic(ordering);
Ordering order;
if (constrainKeys) {
order =
Ordering::ColamdConstrained(affectedFactorsVarIndex, *constrainKeys);
} else {
if (theta_.size() > observedKeys.size()) {
// Only if some variables are unconstrained
FastMap<Key, int> constraintGroups;
for (Key var : observedKeys) constraintGroups[var] = 1;
order = Ordering::ColamdConstrained(affectedFactorsVarIndex,
constraintGroups);
} else {
order = Ordering::Colamd(affectedFactorsVarIndex);
}
}
gttoc(ordering);
gttic(linearize);
GaussianFactorGraph linearized = *nonlinearFactors_.linearize(theta_);
if (params_.cacheLinearizedFactors) linearFactors_ = linearized;
gttoc(linearize);
gttic(eliminate);
ISAM2BayesTree::shared_ptr bayesTree =
ISAM2JunctionTree(
GaussianEliminationTree(linearized, affectedFactorsVarIndex, order))
.eliminate(params_.getEliminationFunction())
.first;
gttoc(eliminate);
gttic(insert);
this->clear();
this->roots_.insert(this->roots_.end(), bayesTree->roots().begin(),
bayesTree->roots().end());
this->nodes_.insert(bayesTree->nodes().begin(), bayesTree->nodes().end());
gttoc(insert);
result->variablesReeliminated = affectedKeysSet->size();
result->factorsRecalculated = nonlinearFactors_.size();
lastAffectedMarkedCount = markedKeys.size();
lastAffectedVariableCount = affectedKeysSet->size();
lastAffectedFactorCount = linearized.size();
// Reeliminated keys for detailed results
if (params_.enableDetailedResults) {
for (Key key : theta_.keys()) {
result->detail->variableStatus[key].isReeliminated = true;
}
}
gttoc(batch);
} else {
gttic(incremental);
// 2. Add the new factors \Factors' into the resulting factor graph
FastList<Key> affectedAndNewKeys;
affectedAndNewKeys.insert(affectedAndNewKeys.end(), affectedKeys.begin(),
affectedKeys.end());
affectedAndNewKeys.insert(affectedAndNewKeys.end(), observedKeys.begin(),
observedKeys.end());
gttic(relinearizeAffected);
GaussianFactorGraph factors(
*relinearizeAffectedFactors(affectedAndNewKeys, relinKeys));
if (debug) factors.print("Relinearized factors: ");
gttoc(relinearizeAffected);
if (debug) {
cout << "Affected keys: ";
for (const Key key : affectedKeys) {
cout << key << " ";
}
cout << endl;
}
// Reeliminated keys for detailed results
if (params_.enableDetailedResults) {
for (Key key : affectedAndNewKeys) {
result->detail->variableStatus[key].isReeliminated = true;
}
}
result->variablesReeliminated = affectedAndNewKeys.size();
result->factorsRecalculated = factors.size();
lastAffectedMarkedCount = markedKeys.size();
lastAffectedVariableCount = affectedKeys.size();
lastAffectedFactorCount = factors.size();
#ifdef PRINT_STATS
// output for generating figures
cout << "linear: #markedKeys: " << markedKeys.size()
<< " #affectedVariables: " << affectedKeys.size()
<< " #affectedFactors: " << factors.size()
<< " maxCliqueSize: " << maxClique << " avgCliqueSize: " << avgClique
<< " #Cliques: " << numCliques << " nnzR: " << nnzR << endl;
#endif
gttic(cached);
// add the cached intermediate results from the boundary of the orphans ...
GaussianFactorGraph cachedBoundary = getCachedBoundaryFactors(orphans);
if (debug) cachedBoundary.print("Boundary factors: ");
factors.push_back(cachedBoundary);
gttoc(cached);
gttic(orphans);
// Add the orphaned subtrees
for (const sharedClique& orphan : orphans)
factors += boost::make_shared<BayesTreeOrphanWrapper<Clique> >(orphan);
gttoc(orphans);
// END OF COPIED CODE
// 3. Re-order and eliminate the factor graph into a Bayes net (Algorithm
// [alg:eliminate]), and re-assemble into a new Bayes tree (Algorithm
// [alg:BayesTree])
gttic(reorder_and_eliminate);
gttic(list_to_set);
// create a partial reordering for the new and contaminated factors
// markedKeys are passed in: those variables will be forced to the end in
// the ordering
affectedKeysSet->insert(markedKeys.begin(), markedKeys.end());
affectedKeysSet->insert(affectedKeys.begin(), affectedKeys.end());
gttoc(list_to_set);
VariableIndex affectedFactorsVarIndex(factors);
gttic(ordering_constraints);
// Create ordering constraints
FastMap<Key, int> constraintGroups;
if (constrainKeys) {
constraintGroups = *constrainKeys;
} else {
constraintGroups = FastMap<Key, int>();
const int group =
observedKeys.size() < affectedFactorsVarIndex.size() ? 1 : 0;
for (Key var : observedKeys)
constraintGroups.insert(make_pair(var, group));
}
// Remove unaffected keys from the constraints
for (FastMap<Key, int>::iterator iter = constraintGroups.begin();
iter != constraintGroups.end();
/*Incremented in loop ++iter*/) {
if (unusedIndices.exists(iter->first) ||
!affectedKeysSet->exists(iter->first))
constraintGroups.erase(iter++);
else
++iter;
}
gttoc(ordering_constraints);
// Generate ordering
gttic(Ordering);
Ordering ordering =
Ordering::ColamdConstrained(affectedFactorsVarIndex, constraintGroups);
gttoc(Ordering);
ISAM2BayesTree::shared_ptr bayesTree =
ISAM2JunctionTree(
GaussianEliminationTree(factors, affectedFactorsVarIndex, ordering))
.eliminate(params_.getEliminationFunction())
.first;
gttoc(reorder_and_eliminate);
gttic(reassemble);
this->roots_.insert(this->roots_.end(), bayesTree->roots().begin(),
bayesTree->roots().end());
this->nodes_.insert(bayesTree->nodes().begin(), bayesTree->nodes().end());
gttoc(reassemble);
// 4. The orphans have already been inserted during elimination
gttoc(incremental);
}
// Root clique variables for detailed results
if (params_.enableDetailedResults) {
for (const sharedNode& root : this->roots())
for (Key var : *root->conditional())
result->detail->variableStatus[var].inRootClique = true;
}
return affectedKeysSet;
}
/* ************************************************************************* */
void ISAM2::addVariables(const Values& newTheta) {
void ISAM2::addVariables(
const Values& newTheta,
ISAM2Result::DetailedResults::StatusMap* variableStatus) {
gttic(addNewVariables);
// \Theta:=\Theta\cup\Theta_{new}.
const bool debug = ISDEBUG("ISAM2 AddVariables");
theta_.insert(newTheta);
@ -490,6 +70,13 @@ void ISAM2::addVariables(const Values& newTheta) {
delta_.insert(newTheta.zeroVectors());
deltaNewton_.insert(newTheta.zeroVectors());
RgProd_.insert(newTheta.zeroVectors());
// New keys for detailed results
if (variableStatus && params_.enableDetailedResults) {
for (Key key : newTheta.keys()) {
(*variableStatus)[key].isNew = true;
}
}
}
/* ************************************************************************* */
@ -506,36 +93,6 @@ void ISAM2::removeVariables(const KeySet& unusedKeys) {
}
}
/* ************************************************************************* */
void ISAM2::expmapMasked(const KeySet& mask) {
assert(theta_.size() == delta_.size());
Values::iterator key_value;
VectorValues::const_iterator key_delta;
#ifdef GTSAM_USE_TBB
for (key_value = theta_.begin(); key_value != theta_.end(); ++key_value) {
key_delta = delta_.find(key_value->key);
#else
for (key_value = theta_.begin(), key_delta = delta_.begin();
key_value != theta_.end(); ++key_value, ++key_delta) {
assert(key_value->key == key_delta->first);
#endif
Key var = key_value->key;
assert(static_cast<size_t>(delta_[var].size()) == key_value->value.dim());
assert(delta_[var].allFinite());
if (mask.exists(var)) {
Value* retracted = key_value->value.retract_(delta_[var]);
key_value->value = *retracted;
retracted->deallocate_();
#ifndef NDEBUG
// If debugging, invalidate delta_ entries to Inf, to trigger assertions
// if we try to re-use them.
delta_[var] = Vector::Constant(delta_[var].rows(),
numeric_limits<double>::infinity());
#endif
}
}
}
/* ************************************************************************* */
ISAM2Result ISAM2::update(
const NonlinearFactorGraph& newFactors, const Values& newTheta,
@ -544,7 +101,6 @@ ISAM2Result ISAM2::update(
const boost::optional<FastList<Key> >& noRelinKeys,
const boost::optional<FastList<Key> >& extraReelimKeys,
bool force_relinearize) {
ISAM2UpdateParams params;
params.constrainedKeys = constrainedKeys;
params.extraReelimKeys = extraReelimKeys;
@ -557,28 +113,21 @@ ISAM2Result ISAM2::update(
/* ************************************************************************* */
ISAM2Result ISAM2::update(const NonlinearFactorGraph& newFactors,
const Values& newTheta,
const ISAM2UpdateParams& updateParams) {
const bool debug = ISDEBUG("ISAM2 update");
const bool verbose = ISDEBUG("ISAM2 update verbose");
const Values& newTheta,
const ISAM2UpdateParams& updateParams) {
gttic(ISAM2_update);
this->update_count_++;
lastAffectedVariableCount = 0;
lastAffectedFactorCount = 0;
lastAffectedCliqueCount = 0;
lastAffectedMarkedCount = 0;
lastBacksubVariableCount = 0;
lastNnzTop = 0;
ISAM2Result result;
if (params_.enableDetailedResults)
result.detail = ISAM2Result::DetailedResults();
const bool relinearizeThisStep =
updateParams.force_relinearize || (params_.enableRelinearization &&
update_count_ % params_.relinearizeSkip == 0);
updateParams.force_relinearize ||
(params_.enableRelinearization &&
update_count_ % params_.relinearizeSkip == 0);
const bool verbose = ISDEBUG("ISAM2 update verbose");
if (verbose) {
cout << "ISAM2::update\n";
this->print("ISAM2: ");
@ -586,243 +135,66 @@ ISAM2Result ISAM2::update(const NonlinearFactorGraph& newFactors,
// Update delta if we need it to check relinearization later
if (relinearizeThisStep) {
gttic(updateDelta);
updateDelta(kDisableReordering);
gttoc(updateDelta);
updateDelta(updateParams.forceFullSolve);
}
gttic(push_back_factors);
UpdateImpl update(params_, updateParams);
// 1. Add any new factors \Factors:=\Factors\cup\Factors'.
// Add the new factor indices to the result struct
if (debug || verbose) newFactors.print("The new factors are: ");
Impl::AddFactorsStep1(newFactors, params_.findUnusedFactorSlots,
&nonlinearFactors_, &result.newFactorsIndices);
update.pushBackFactors(newFactors, &nonlinearFactors_, &linearFactors_,
&variableIndex_, &result);
// Remove the removed factors
NonlinearFactorGraph removeFactors;
removeFactors.reserve(updateParams.removeFactorIndices.size());
for (const auto index : updateParams.removeFactorIndices) {
removeFactors.push_back(nonlinearFactors_[index]);
nonlinearFactors_.remove(index);
if (params_.cacheLinearizedFactors) linearFactors_.remove(index);
}
// Remove removed factors from the variable index so we do not attempt to
// relinearize them
variableIndex_.remove(updateParams.removeFactorIndices.begin(),
updateParams.removeFactorIndices.end(),
removeFactors);
// Compute unused keys and indices
KeySet unusedKeys;
KeySet unusedIndices;
{
// Get keys from removed factors and new factors, and compute unused keys,
// i.e., keys that are empty now and do not appear in the new factors.
KeySet removedAndEmpty;
for (Key key : removeFactors.keys()) {
if (variableIndex_[key].empty())
removedAndEmpty.insert(removedAndEmpty.end(), key);
}
KeySet newFactorSymbKeys = newFactors.keys();
std::set_difference(removedAndEmpty.begin(), removedAndEmpty.end(),
newFactorSymbKeys.begin(), newFactorSymbKeys.end(),
std::inserter(unusedKeys, unusedKeys.end()));
// Get indices for unused keys
for (Key key : unusedKeys) {
unusedIndices.insert(unusedIndices.end(), key);
}
}
gttoc(push_back_factors);
gttic(add_new_variables);
// 2. Initialize any new variables \Theta_{new} and add
// \Theta:=\Theta\cup\Theta_{new}.
addVariables(newTheta);
// New keys for detailed results
if (params_.enableDetailedResults) {
for (Key key : newTheta.keys()) {
result.detail->variableStatus[key].isNew = true;
}
}
gttoc(add_new_variables);
addVariables(newTheta, result.detail ? &result.detail->variableStatus : 0);
gttic(evaluate_error_before);
if (params_.evaluateNonlinearError)
result.errorBefore.reset(nonlinearFactors_.error(calculateEstimate()));
gttoc(evaluate_error_before);
gttic(gather_involved_keys);
// 3. Mark linear update
KeySet markedKeys = newFactors.keys(); // Get keys from new factors
// Also mark keys involved in removed factors
{
KeySet markedRemoveKeys =
removeFactors.keys(); // Get keys involved in removed factors
markedKeys.insert(
markedRemoveKeys.begin(),
markedRemoveKeys.end()); // Add to the overall set of marked keys
}
// Also mark any provided extra re-eliminate keys
if (updateParams.extraReelimKeys) {
for (Key key : *updateParams.extraReelimKeys) {
markedKeys.insert(key);
}
}
// Also, keys that were not observed in existing factors, but whose affected
// keys have been extended now (e.g. smart factors)
if (updateParams.newAffectedKeys) {
for (const auto &factorAddedKeys : *updateParams.newAffectedKeys) {
const auto factorIdx = factorAddedKeys.first;
const auto& affectedKeys = nonlinearFactors_.at(factorIdx)->keys();
markedKeys.insert(affectedKeys.begin(),affectedKeys.end());
}
}
// Observed keys for detailed results
if (params_.enableDetailedResults) {
for (Key key : markedKeys) {
result.detail->variableStatus[key].isObserved = true;
}
}
KeyVector observedKeys;
for (Key index : markedKeys) {
// Only add if not unused
if (unusedIndices.find(index) == unusedIndices.end())
// Make a copy of these, as we'll soon add to them
observedKeys.push_back(index);
}
gttoc(gather_involved_keys);
update.gatherInvolvedKeys(newFactors, nonlinearFactors_, &result);
// Check relinearization if we're at the nth step, or we are using a looser
// loop relin threshold
KeySet relinKeys;
if (relinearizeThisStep) {
gttic(gather_relinearize_keys);
// 4. Mark keys in \Delta above threshold \beta:
// J=\{\Delta_{j}\in\Delta|\Delta_{j}\geq\beta\}.
if (params_.enablePartialRelinearizationCheck)
relinKeys = Impl::CheckRelinearizationPartial(
roots_, delta_, params_.relinearizeThreshold);
else
relinKeys =
Impl::CheckRelinearizationFull(delta_, params_.relinearizeThreshold);
if (kDisableReordering)
relinKeys = Impl::CheckRelinearizationFull(
delta_, 0.0); // This is used for debugging
// Remove from relinKeys any keys whose linearization points are fixed
for (Key key : fixedVariables_) {
relinKeys.erase(key);
}
if (updateParams.noRelinKeys) {
for (Key key : *updateParams.noRelinKeys) {
relinKeys.erase(key);
}
}
// Above relin threshold keys for detailed results
if (params_.enableDetailedResults) {
for (Key key : relinKeys) {
result.detail->variableStatus[key].isAboveRelinThreshold = true;
result.detail->variableStatus[key].isRelinearized = true;
}
}
// Add the variables being relinearized to the marked keys
KeySet markedRelinMask;
for (const Key key : relinKeys) markedRelinMask.insert(key);
markedKeys.insert(relinKeys.begin(), relinKeys.end());
gttoc(gather_relinearize_keys);
gttic(fluid_find_all);
// 5. Mark all cliques that involve marked variables \Theta_{J} and all
// their ancestors.
if (!relinKeys.empty()) {
for (const sharedClique& root : roots_)
// add other cliques that have the marked ones in the separator
root->findAll(markedRelinMask, &markedKeys);
// Relin involved keys for detailed results
if (params_.enableDetailedResults) {
KeySet involvedRelinKeys;
for (const sharedClique& root : roots_)
root->findAll(markedRelinMask, &involvedRelinKeys);
for (Key key : involvedRelinKeys) {
if (!result.detail->variableStatus[key].isAboveRelinThreshold) {
result.detail->variableStatus[key].isRelinearizeInvolved = true;
result.detail->variableStatus[key].isRelinearized = true;
}
}
}
}
gttoc(fluid_find_all);
gttic(expmap);
// 6. Update linearization point for marked variables:
// \Theta_{J}:=\Theta_{J}+\Delta_{J}.
if (!relinKeys.empty()) expmapMasked(markedRelinMask);
gttoc(expmap);
result.variablesRelinearized = markedKeys.size();
relinKeys =
update.relinearize(roots_, delta_, fixedVariables_, &theta_, &result);
} else {
result.variablesRelinearized = 0;
}
gttic(linearize_new);
// 7. Linearize new factors
if (params_.cacheLinearizedFactors) {
gttic(linearize);
auto linearFactors = newFactors.linearize(theta_);
if (params_.findUnusedFactorSlots) {
linearFactors_.resize(nonlinearFactors_.size());
for (size_t newFactorI = 0; newFactorI < newFactors.size(); ++newFactorI)
linearFactors_[result.newFactorsIndices[newFactorI]] =
(*linearFactors)[newFactorI];
} else {
linearFactors_.push_back(*linearFactors);
}
assert(nonlinearFactors_.size() == linearFactors_.size());
gttoc(linearize);
}
gttoc(linearize_new);
update.linearizeNewFactors(newFactors, theta_, nonlinearFactors_.size(),
result.newFactorsIndices, &linearFactors_);
update.augmentVariableIndex(newFactors, result.newFactorsIndices,
&variableIndex_);
gttic(augment_VI);
// Augment the variable index with the new factors
if (params_.findUnusedFactorSlots)
variableIndex_.augment(newFactors, result.newFactorsIndices);
else
variableIndex_.augment(newFactors);
// Augment it with existing factors which now affect to more variables:
if (updateParams.newAffectedKeys) {
for (const auto &factorAddedKeys : *updateParams.newAffectedKeys) {
const auto factorIdx = factorAddedKeys.first;
variableIndex_.augmentExistingFactor(
factorIdx, factorAddedKeys.second);
}
}
gttoc(augment_VI);
gttic(recalculate);
// 8. Redo top of Bayes tree
boost::shared_ptr<KeySet> replacedKeys;
if (!markedKeys.empty() || !observedKeys.empty())
replacedKeys = recalculate(
markedKeys, relinKeys, observedKeys, unusedIndices,
updateParams.constrainedKeys, &result);
if (!result.markedKeys.empty() || !result.observedKeys.empty()) {
// Remove top of Bayes tree and convert to a factor graph:
// (a) For each affected variable, remove the corresponding clique and all
// parents up to the root. (b) Store orphaned sub-trees \BayesTree_{O} of
// removed cliques.
GaussianBayesNet affectedBayesNet;
Cliques orphans;
this->removeTop(
KeyVector(result.markedKeys.begin(), result.markedKeys.end()),
affectedBayesNet, orphans);
// Update replaced keys mask (accumulates until back-substitution takes place)
if (replacedKeys)
deltaReplacedMask_.insert(replacedKeys->begin(), replacedKeys->end());
gttoc(recalculate);
KeySet affectedKeysSet = update.recalculate(
theta_, variableIndex_, nonlinearFactors_, affectedBayesNet, orphans,
relinKeys, &linearFactors_, &roots_, &nodes_, &result);
// Update replaced keys mask (accumulates until back-substitution takes
// place)
deltaReplacedMask_.insert(affectedKeysSet.begin(), affectedKeysSet.end());
}
// Update data structures to remove unused keys
if (!unusedKeys.empty()) {
if (!result.unusedKeys.empty()) {
gttic(remove_variables);
removeVariables(unusedKeys);
removeVariables(result.unusedKeys);
gttoc(remove_variables);
}
result.cliques = this->nodes().size();
@ -966,7 +338,7 @@ void ISAM2::marginalizeLeaves(
}
}
// Create factor graph from factor indices
for (const auto index: factorsFromMarginalizedInClique_step1) {
for (const auto index : factorsFromMarginalizedInClique_step1) {
graph.push_back(nonlinearFactors_[index]->linearize(theta_));
}
@ -1040,7 +412,7 @@ void ISAM2::marginalizeLeaves(
// Remove the factors to remove that have been summarized in the newly-added
// marginal factors
NonlinearFactorGraph removedFactors;
for (const auto index: factorIndicesToRemove) {
for (const auto index : factorIndicesToRemove) {
removedFactors.push_back(nonlinearFactors_[index]);
nonlinearFactors_.remove(index);
if (params_.cacheLinearizedFactors) linearFactors_.remove(index);
@ -1057,6 +429,7 @@ void ISAM2::marginalizeLeaves(
}
/* ************************************************************************* */
// Marked const but actually changes mutable delta
void ISAM2::updateDelta(bool forceFullSolve) const {
gttic(updateDelta);
if (params_.optimizationParams.type() == typeid(ISAM2GaussNewtonParams)) {
@ -1066,8 +439,8 @@ void ISAM2::updateDelta(bool forceFullSolve) const {
const double effectiveWildfireThreshold =
forceFullSolve ? 0.0 : gaussNewtonParams.wildfireThreshold;
gttic(Wildfire_update);
lastBacksubVariableCount = Impl::UpdateGaussNewtonDelta(
roots_, deltaReplacedMask_, effectiveWildfireThreshold, &delta_);
DeltaImpl::UpdateGaussNewtonDelta(roots_, deltaReplacedMask_,
effectiveWildfireThreshold, &delta_);
deltaReplacedMask_.clear();
gttoc(Wildfire_update);
@ -1083,15 +456,15 @@ void ISAM2::updateDelta(bool forceFullSolve) const {
// Compute Newton's method step
gttic(Wildfire_update);
lastBacksubVariableCount = Impl::UpdateGaussNewtonDelta(
DeltaImpl::UpdateGaussNewtonDelta(
roots_, deltaReplacedMask_, effectiveWildfireThreshold, &deltaNewton_);
gttoc(Wildfire_update);
// Compute steepest descent step
const VectorValues gradAtZero = this->gradientAtZero(); // Compute gradient
Impl::UpdateRgProd(roots_, deltaReplacedMask_, gradAtZero,
&RgProd_); // Update RgProd
const VectorValues dx_u = Impl::ComputeGradientSearch(
DeltaImpl::UpdateRgProd(roots_, deltaReplacedMask_, gradAtZero,
&RgProd_); // Update RgProd
const VectorValues dx_u = DeltaImpl::ComputeGradientSearch(
gradAtZero, RgProd_); // Compute gradient search point
// Clear replaced keys mask because now we've updated deltaNewton_ and
@ -1163,8 +536,7 @@ VectorValues ISAM2::gradientAtZero() const {
VectorValues g;
// Sum up contributions for each clique
for (const auto& root : this->roots())
root->addGradientAtZero(&g);
for (const auto& root : this->roots()) root->addGradientAtZero(&g);
return g;
}

55
gtsam/nonlinear/ISAM2.h
View File

@ -20,12 +20,12 @@
#pragma once
#include <gtsam/linear/GaussianBayesTree.h>
#include <gtsam/nonlinear/ISAM2Clique.h>
#include <gtsam/nonlinear/ISAM2Params.h>
#include <gtsam/nonlinear/ISAM2Result.h>
#include <gtsam/nonlinear/ISAM2Clique.h>
#include <gtsam/nonlinear/ISAM2UpdateParams.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/linear/GaussianBayesTree.h>
#include <vector>
@ -73,8 +73,8 @@ class GTSAM_EXPORT ISAM2 : public BayesTree<ISAM2Clique> {
* This is \c mutable because it is used internally to not update delta_
* until it is needed.
*/
mutable KeySet
deltaReplacedMask_; // TODO(dellaert): Make sure accessed in the right way
mutable KeySet deltaReplacedMask_; // TODO(dellaert): Make sure accessed in
// the right way
/** All original nonlinear factors are stored here to use during
* relinearization */
@ -175,9 +175,9 @@ class GTSAM_EXPORT ISAM2 : public BayesTree<ISAM2Clique> {
* @return An ISAM2Result struct containing information about the update
* @note No default parameters to avoid ambiguous call errors.
*/
virtual ISAM2Result update(
const NonlinearFactorGraph& newFactors, const Values& newTheta,
const ISAM2UpdateParams& updateParams);
virtual ISAM2Result update(const NonlinearFactorGraph& newFactors,
const Values& newTheta,
const ISAM2UpdateParams& updateParams);
/** Marginalize out variables listed in leafKeys. These keys must be leaves
* in the BayesTree. Throws MarginalizeNonleafException if non-leaves are
@ -226,7 +226,6 @@ class GTSAM_EXPORT ISAM2 : public BayesTree<ISAM2Clique> {
return traits<VALUE>::Retract(theta_.at<VALUE>(key), delta);
}
/** Compute an estimate for a single variable using its incomplete linear
* delta computed during the last update. This is faster than calling the
* no-argument version of calculateEstimate, which operates on all variables.
@ -243,9 +242,6 @@ class GTSAM_EXPORT ISAM2 : public BayesTree<ISAM2Clique> {
/// @name Public members for non-typical usage
/// @{
/** Internal implementation functions */
struct Impl;
/** Compute an estimate using a complete delta computed by a full
* back-substitution.
*/
@ -268,13 +264,6 @@ class GTSAM_EXPORT ISAM2 : public BayesTree<ISAM2Clique> {
/** Access the nonlinear variable index */
const KeySet& getFixedVariables() const { return fixedVariables_; }
size_t lastAffectedVariableCount;
size_t lastAffectedFactorCount;
size_t lastAffectedCliqueCount;
size_t lastAffectedMarkedCount;
mutable size_t lastBacksubVariableCount;
size_t lastNnzTop;
const ISAM2Params& params() const { return params_; }
/** prints out clique statistics */
@ -295,37 +284,17 @@ class GTSAM_EXPORT ISAM2 : public BayesTree<ISAM2Clique> {
/**
* Add new variables to the ISAM2 system.
* @param newTheta Initial values for new variables
* @param theta Current solution to be augmented with new initialization
* @param delta Current linear delta to be augmented with zeros
* @param deltaNewton
* @param RgProd
* @param keyFormatter Formatter for printing nonlinear keys during debugging
* @param variableStatus optional detailed result structure
*/
void addVariables(const Values& newTheta);
void addVariables(
const Values& newTheta,
ISAM2Result::DetailedResults::StatusMap* variableStatus = 0);
/**
* Remove variables from the ISAM2 system.
*/
void removeVariables(const KeySet& unusedKeys);
/**
* Apply expmap to the given values, but only for indices appearing in
* \c mask. Values are expmapped in-place.
* \param mask Mask on linear indices, only \c true entries are expmapped
*/
void expmapMasked(const KeySet& mask);
FactorIndexSet getAffectedFactors(const FastList<Key>& keys) const;
GaussianFactorGraph::shared_ptr relinearizeAffectedFactors(
const FastList<Key>& affectedKeys, const KeySet& relinKeys) const;
GaussianFactorGraph getCachedBoundaryFactors(const Cliques& orphans);
virtual boost::shared_ptr<KeySet> recalculate(
const KeySet& markedKeys, const KeySet& relinKeys,
const KeyVector& observedKeys, const KeySet& unusedIndices,
const boost::optional<FastMap<Key, int> >& constrainKeys,
ISAM2Result* result);
void updateDelta(bool forceFullSolve = false) const;
}; // ISAM2
@ -334,5 +303,3 @@ template <>
struct traits<ISAM2> : public Testable<ISAM2> {};
} // namespace gtsam
#include <gtsam/nonlinear/ISAM2-impl.h>

23
gtsam/nonlinear/ISAM2Result.h
View File

@ -24,8 +24,8 @@
#include <gtsam/linear/GaussianBayesTree.h>
#include <gtsam/nonlinear/DoglegOptimizerImpl.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/ISAM2Params.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <boost/variant.hpp>
@ -96,6 +96,20 @@ struct GTSAM_EXPORT ISAM2Result {
*/
FactorIndices newFactorsIndices;
/** Unused keys, and indices for unused keys. TODO(frank): the same??
* i.e., keys that are empty now and do not appear in the new factors.
*/
KeySet unusedKeys, unusedIndices;
/** keys for variables that were observed, i.e., not unused. */
KeyVector observedKeys;
/** Keys of variables that had factors removed. */
KeySet keysWithRemovedFactors;
/** All keys that were marked during the update process. */
KeySet markedKeys;
/** A struct holding detailed results, which must be enabled with
* ISAM2Params::enableDetailedResults.
*/
@ -132,9 +146,10 @@ struct GTSAM_EXPORT ISAM2Result {
inRootClique(false) {}
};
/** The status of each variable during this update, see VariableStatus.
*/
FastMap<Key, VariableStatus> variableStatus;
typedef FastMap<Key, VariableStatus> StatusMap;
/// The status of each variable during this update, see VariableStatus.
StatusMap variableStatus;
};
/** Detailed results, if enabled by ISAM2Params::enableDetailedResults. See

16
gtsam/nonlinear/ISAM2UpdateParams.h
View File

@ -16,11 +16,11 @@
#pragma once
#include <boost/optional.hpp>
#include <gtsam/base/FastList.h>
#include <gtsam/dllexport.h> // GTSAM_EXPORT
#include <gtsam/inference/Key.h> // Key, KeySet
#include <gtsam/nonlinear/ISAM2Result.h> //FactorIndices
#include <gtsam/dllexport.h> // GTSAM_EXPORT
#include <gtsam/inference/Key.h> // Key, KeySet
#include <gtsam/nonlinear/ISAM2Result.h> //FactorIndices
#include <boost/optional.hpp>
namespace gtsam {
@ -63,8 +63,12 @@ struct GTSAM_EXPORT ISAM2UpdateParams {
* depend on Keys `X(2)`, `X(3)`. Next call to ISAM2::update() must include
* its `newAffectedKeys` field with the map `13 -> {X(2), X(3)}`.
*/
boost::optional<FastMap<FactorIndex,KeySet>> newAffectedKeys{boost::none};
boost::optional<FastMap<FactorIndex, KeySet>> newAffectedKeys{boost::none};
/** By default, iSAM2 uses a wildfire update scheme that stops updating when
* the deltas become too small down in the tree. This flagg forces a full
* solve instead. */
bool forceFullSolve{false};
};
} // namespace gtsam
} // namespace gtsam

13
tests/testGaussianISAM2.cpp
View File

@ -4,7 +4,8 @@
* @author Michael Kaess
*/
#include <CppUnitLite/TestHarness.h>
#include <gtsam/nonlinear/ISAM2.h>
#include <gtsam/nonlinear/ISAM2-impl.h>
#include <tests/smallExample.h>
#include <gtsam/slam/PriorFactor.h>
@ -14,7 +15,6 @@
#include <gtsam/geometry/Pose2.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/ISAM2.h>
#include <gtsam/nonlinear/Marginals.h>
#include <gtsam/linear/GaussianBayesNet.h>
#include <gtsam/linear/GaussianBayesTree.h>
@ -23,10 +23,13 @@
#include <gtsam/base/debug.h>
#include <gtsam/base/TestableAssertions.h>
#include <gtsam/base/treeTraversal-inst.h>
#include <boost/assign/list_of.hpp>
#include <gtsam/base/deprecated/LieScalar.h>
using namespace boost::assign;
#include <CppUnitLite/TestHarness.h>
#include <boost/assign/list_of.hpp>
#include <boost/range/adaptor/map.hpp>
using namespace boost::assign;
namespace br { using namespace boost::adaptors; using namespace boost::range; }
using namespace std;
@ -304,7 +307,7 @@ TEST(ISAM2, AddFactorsStep1)
FactorIndices actualNewFactorIndices;
ISAM2::Impl::AddFactorsStep1(newFactors, true, &nonlinearFactors, &actualNewFactorIndices);
UpdateImpl::AddFactorsStep1(newFactors, true, &nonlinearFactors, &actualNewFactorIndices);
EXPECT(assert_equal(expectedNonlinearFactors, nonlinearFactors));
EXPECT(assert_container_equality(expectedNewFactorIndices, actualNewFactorIndices));