Merge pull request #619 from borglab/fix/zero_translation_avg
Handling edges with pure rotation in translation averagingrelease/4.3a0
Akshay Krishnan
GitHub
commit
d6f7da73c3
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@ -11,13 +11,12 @@
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/**
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* @file TranslationRecovery.cpp
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* @author Frank Dellaert
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* @author Frank Dellaert, Akshay Krishnan
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* @date March 2020
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* @brief Source code for recovering translations when rotations are given
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*/
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#include <gtsam/sfm/TranslationRecovery.h>
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#include <gtsam/base/DSFMap.h>
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#include <gtsam/geometry/Point3.h>
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#include <gtsam/geometry/Pose3.h>
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#include <gtsam/geometry/Unit3.h>
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@ -27,11 +26,45 @@
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#include <gtsam/nonlinear/NonlinearFactorGraph.h>
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#include <gtsam/nonlinear/Values.h>
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#include <gtsam/sfm/TranslationFactor.h>
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#include <gtsam/sfm/TranslationRecovery.h>
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#include <gtsam/slam/PriorFactor.h>
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#include <set>
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#include <utility>
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using namespace gtsam;
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using namespace std;
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TranslationRecovery::TranslationRecovery(
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const TranslationRecovery::TranslationEdges &relativeTranslations,
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const LevenbergMarquardtParams &lmParams)
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: params_(lmParams) {
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// Some relative translations may be zero. We treat nodes that have a zero
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// relativeTranslation as a single node.
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// A DSFMap is used to find sets of nodes that have a zero relative
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// translation. Add the nodes in each edge to the DSFMap, and merge nodes that
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// are connected by a zero relative translation.
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DSFMap<Key> sameTranslationDSF;
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for (const auto &edge : relativeTranslations) {
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Key key1 = sameTranslationDSF.find(edge.key1());
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Key key2 = sameTranslationDSF.find(edge.key2());
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if (key1 != key2 && edge.measured().equals(Unit3(0.0, 0.0, 0.0))) {
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sameTranslationDSF.merge(key1, key2);
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}
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}
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// Use only those edges for which two keys have a distinct root in the DSFMap.
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for (const auto &edge : relativeTranslations) {
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Key key1 = sameTranslationDSF.find(edge.key1());
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Key key2 = sameTranslationDSF.find(edge.key2());
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if (key1 == key2) continue;
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relativeTranslations_.emplace_back(key1, key2, edge.measured(),
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edge.noiseModel());
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}
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// Store the DSF map for post-processing results.
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sameTranslationNodes_ = sameTranslationDSF.sets();
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}
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NonlinearFactorGraph TranslationRecovery::buildGraph() const {
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NonlinearFactorGraph graph;
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@ -44,13 +77,14 @@ NonlinearFactorGraph TranslationRecovery::buildGraph() const {
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return graph;
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}
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void TranslationRecovery::addPrior(const double scale,
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NonlinearFactorGraph *graph,
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const SharedNoiseModel &priorNoiseModel) const {
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void TranslationRecovery::addPrior(
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const double scale, NonlinearFactorGraph *graph,
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const SharedNoiseModel &priorNoiseModel) const {
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auto edge = relativeTranslations_.begin();
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graph->emplace_shared<PriorFactor<Point3> >(edge->key1(), Point3(0, 0, 0), priorNoiseModel);
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graph->emplace_shared<PriorFactor<Point3> >(edge->key2(), scale * edge->measured().point3(),
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edge->noiseModel());
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graph->emplace_shared<PriorFactor<Point3> >(edge->key1(), Point3(0, 0, 0),
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priorNoiseModel);
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graph->emplace_shared<PriorFactor<Point3> >(
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edge->key2(), scale * edge->measured().point3(), edge->noiseModel());
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}
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Values TranslationRecovery::initalizeRandomly() const {
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@ -77,6 +111,19 @@ Values TranslationRecovery::run(const double scale) const {
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const Values initial = initalizeRandomly();
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LevenbergMarquardtOptimizer lm(graph, initial, params_);
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Values result = lm.optimize();
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// Nodes that were not optimized are stored in sameTranslationNodes_ as a map
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// from a key that was optimized to keys that were not optimized. Iterate over
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// map and add results for keys not optimized.
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for (const auto &optimizedAndDuplicateKeys : sameTranslationNodes_) {
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Key optimizedKey = optimizedAndDuplicateKeys.first;
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std::set<Key> duplicateKeys = optimizedAndDuplicateKeys.second;
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// Add the result for the duplicate key if it does not already exist.
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for (const Key duplicateKey : duplicateKeys) {
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if (result.exists(duplicateKey)) continue;
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result.insert<Point3>(duplicateKey, result.at<Point3>(optimizedKey));
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}
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}
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return result;
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}
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@ -16,14 +16,16 @@
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* @brief Recovering translations in an epipolar graph when rotations are given.
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*/
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#include <map>
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#include <set>
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#include <utility>
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#include <vector>
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#include <gtsam/geometry/Unit3.h>
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#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
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#include <gtsam/nonlinear/Values.h>
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#include <gtsam/sfm/BinaryMeasurement.h>
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#include <utility>
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#include <vector>
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namespace gtsam {
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// Set up an optimization problem for the unknown translations Ti in the world
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@ -52,23 +54,30 @@ class TranslationRecovery {
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using TranslationEdges = std::vector<BinaryMeasurement<Unit3>>;
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private:
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// Translation directions between camera pairs.
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TranslationEdges relativeTranslations_;
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// Parameters used by the LM Optimizer.
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LevenbergMarquardtParams params_;
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// Map from a key in the graph to a set of keys that share the same
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// translation.
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std::map<Key, std::set<Key>> sameTranslationNodes_;
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public:
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/**
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* @brief Construct a new Translation Recovery object
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*
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* @param relativeTranslations the relative translations, in world coordinate
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* frames, vector of BinaryMeasurements of Unit3, where each key of a measurement
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* is a point in 3D.
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* frames, vector of BinaryMeasurements of Unit3, where each key of a
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* measurement is a point in 3D.
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* @param lmParams (optional) gtsam::LavenbergMarquardtParams that can be
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* used to modify the parameters for the LM optimizer. By default, uses the
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* default LM parameters.
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* default LM parameters.
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*/
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TranslationRecovery(const TranslationEdges &relativeTranslations,
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const LevenbergMarquardtParams &lmParams = LevenbergMarquardtParams())
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: relativeTranslations_(relativeTranslations), params_(lmParams) {}
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TranslationRecovery(
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const TranslationEdges &relativeTranslations,
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const LevenbergMarquardtParams &lmParams = LevenbergMarquardtParams());
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/**
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* @brief Build the factor graph to do the optimization.
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@ -108,8 +117,8 @@ class TranslationRecovery {
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*
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* @param poses SE(3) ground truth poses stored as Values
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* @param edges pairs (a,b) for which a measurement w_aZb will be generated.
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* @return TranslationEdges vector of binary measurements where the keys are
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* the cameras and the measurement is the simulated Unit3 translation
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* @return TranslationEdges vector of binary measurements where the keys are
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* the cameras and the measurement is the simulated Unit3 translation
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* direction between the cameras.
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*/
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static TranslationEdges SimulateMeasurements(
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@ -11,19 +11,29 @@
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/**
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* @file testTranslationRecovery.cpp
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* @author Frank Dellaert
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* @author Frank Dellaert, Akshay Krishnan
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* @date March 2020
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* @brief test recovering translations when rotations are given.
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*/
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#include <gtsam/sfm/TranslationRecovery.h>
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#include <CppUnitLite/TestHarness.h>
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#include <gtsam/sfm/TranslationRecovery.h>
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#include <gtsam/slam/dataset.h>
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using namespace std;
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using namespace gtsam;
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// Returns the Unit3 direction as measured in the binary measurement, but
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// computed from the input poses. Helper function used in the unit tests.
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Unit3 GetDirectionFromPoses(const Values& poses,
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const BinaryMeasurement<Unit3>& unitTranslation) {
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const Pose3 wTa = poses.at<Pose3>(unitTranslation.key1()),
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wTb = poses.at<Pose3>(unitTranslation.key2());
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const Point3 Ta = wTa.translation(), Tb = wTb.translation();
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return Unit3(Tb - Ta);
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}
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/* ************************************************************************* */
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// We read the BAL file, which has 3 cameras in it, with poses. We then assume
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// the rotations are correct, but translations have to be estimated from
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@ -48,43 +58,186 @@ TEST(TranslationRecovery, BAL) {
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const auto relativeTranslations = TranslationRecovery::SimulateMeasurements(
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poses, {{0, 1}, {0, 2}, {1, 2}});
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// Check
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Unit3 w_aZb_stored; // measurement between 0 and 1 stored for next unit test
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for(auto& unitTranslation : relativeTranslations) {
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const Pose3 wTa = poses.at<Pose3>(unitTranslation.key1()),
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wTb = poses.at<Pose3>(unitTranslation.key2());
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const Point3 Ta = wTa.translation(), Tb = wTb.translation();
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const Unit3 w_aZb = unitTranslation.measured();
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EXPECT(assert_equal(Unit3(Tb - Ta), w_aZb));
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if(unitTranslation.key1() == 0 && unitTranslation.key2() == 1) {
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w_aZb_stored = unitTranslation.measured();
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}
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// Check simulated measurements.
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for (auto& unitTranslation : relativeTranslations) {
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EXPECT(assert_equal(GetDirectionFromPoses(poses, unitTranslation),
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unitTranslation.measured()));
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}
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TranslationRecovery algorithm(relativeTranslations);
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const auto graph = algorithm.buildGraph();
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EXPECT_LONGS_EQUAL(3, graph.size());
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// Translation recovery, version 1
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// Run translation recovery
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const double scale = 2.0;
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const auto result = algorithm.run(scale);
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// Check result for first two translations, determined by prior
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EXPECT(assert_equal(Point3(0, 0, 0), result.at<Point3>(0)));
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EXPECT(assert_equal(Point3(2 * w_aZb_stored.point3()), result.at<Point3>(1)));
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EXPECT(assert_equal(
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Point3(2 * GetDirectionFromPoses(poses, relativeTranslations[0])),
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result.at<Point3>(1)));
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// Check that the third translations is correct
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Point3 Ta = poses.at<Pose3>(0).translation();
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Point3 Tb = poses.at<Pose3>(1).translation();
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Point3 Tc = poses.at<Pose3>(2).translation();
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Point3 expected =
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(Tc - Ta) * (scale / (Tb - Ta).norm());
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Point3 expected = (Tc - Ta) * (scale / (Tb - Ta).norm());
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EXPECT(assert_equal(expected, result.at<Point3>(2), 1e-4));
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// TODO(frank): how to get stats back?
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// EXPECT_DOUBLES_EQUAL(0.0199833, actualError, 1e-5);
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}
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TEST(TranslationRecovery, TwoPoseTest) {
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// Create a dataset with 2 poses.
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// __ __
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// \/ \/
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// 0 _____ 1
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//
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// 0 and 1 face in the same direction but have a translation offset.
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Values poses;
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poses.insert<Pose3>(0, Pose3(Rot3(), Point3(0, 0, 0)));
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poses.insert<Pose3>(1, Pose3(Rot3(), Point3(2, 0, 0)));
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auto relativeTranslations =
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TranslationRecovery::SimulateMeasurements(poses, {{0, 1}});
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// Check simulated measurements.
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for (auto& unitTranslation : relativeTranslations) {
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EXPECT(assert_equal(GetDirectionFromPoses(poses, unitTranslation),
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unitTranslation.measured()));
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}
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TranslationRecovery algorithm(relativeTranslations);
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const auto graph = algorithm.buildGraph();
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EXPECT_LONGS_EQUAL(1, graph.size());
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// Run translation recovery
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const auto result = algorithm.run(/*scale=*/3.0);
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// Check result for first two translations, determined by prior
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EXPECT(assert_equal(Point3(0, 0, 0), result.at<Point3>(0)));
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EXPECT(assert_equal(Point3(3, 0, 0), result.at<Point3>(1)));
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}
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TEST(TranslationRecovery, ThreePoseTest) {
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// Create a dataset with 3 poses.
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// __ __
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// \/ \/
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// 0 _____ 1
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// \ __ /
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// \\//
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// 3
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//
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// 0 and 1 face in the same direction but have a translation offset. 3 is in
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// the same direction as 0 and 1, in between 0 and 1, with some Y axis offset.
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Values poses;
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poses.insert<Pose3>(0, Pose3(Rot3(), Point3(0, 0, 0)));
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poses.insert<Pose3>(1, Pose3(Rot3(), Point3(2, 0, 0)));
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poses.insert<Pose3>(3, Pose3(Rot3(), Point3(1, -1, 0)));
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auto relativeTranslations = TranslationRecovery::SimulateMeasurements(
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poses, {{0, 1}, {1, 3}, {3, 0}});
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// Check simulated measurements.
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for (auto& unitTranslation : relativeTranslations) {
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EXPECT(assert_equal(GetDirectionFromPoses(poses, unitTranslation),
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unitTranslation.measured()));
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}
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TranslationRecovery algorithm(relativeTranslations);
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const auto graph = algorithm.buildGraph();
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EXPECT_LONGS_EQUAL(3, graph.size());
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const auto result = algorithm.run(/*scale=*/3.0);
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// Check result
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EXPECT(assert_equal(Point3(0, 0, 0), result.at<Point3>(0)));
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EXPECT(assert_equal(Point3(3, 0, 0), result.at<Point3>(1)));
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EXPECT(assert_equal(Point3(1.5, -1.5, 0), result.at<Point3>(3)));
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}
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TEST(TranslationRecovery, ThreePosesIncludingZeroTranslation) {
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// Create a dataset with 3 poses.
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// __ __
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// \/ \/
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// 0 _____ 1
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// 2 <|
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//
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// 0 and 1 face in the same direction but have a translation offset. 2 is at
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// the same point as 1 but is rotated, with little FOV overlap.
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Values poses;
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poses.insert<Pose3>(0, Pose3(Rot3(), Point3(0, 0, 0)));
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poses.insert<Pose3>(1, Pose3(Rot3(), Point3(2, 0, 0)));
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poses.insert<Pose3>(2, Pose3(Rot3::RzRyRx(-M_PI / 2, 0, 0), Point3(2, 0, 0)));
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auto relativeTranslations =
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TranslationRecovery::SimulateMeasurements(poses, {{0, 1}, {1, 2}});
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// Check simulated measurements.
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for (auto& unitTranslation : relativeTranslations) {
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EXPECT(assert_equal(GetDirectionFromPoses(poses, unitTranslation),
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unitTranslation.measured()));
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}
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TranslationRecovery algorithm(relativeTranslations);
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const auto graph = algorithm.buildGraph();
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// There is only 1 non-zero translation edge.
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EXPECT_LONGS_EQUAL(1, graph.size());
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// Run translation recovery
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const auto result = algorithm.run(/*scale=*/3.0);
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// Check result
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EXPECT(assert_equal(Point3(0, 0, 0), result.at<Point3>(0)));
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EXPECT(assert_equal(Point3(3, 0, 0), result.at<Point3>(1)));
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EXPECT(assert_equal(Point3(3, 0, 0), result.at<Point3>(2)));
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}
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TEST(TranslationRecovery, FourPosesIncludingZeroTranslation) {
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// Create a dataset with 4 poses.
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// __ __
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// \/ \/
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// 0 _____ 1
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// \ __ 2 <|
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// \\//
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// 3
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//
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// 0 and 1 face in the same direction but have a translation offset. 2 is at
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// the same point as 1 but is rotated, with very little FOV overlap. 3 is in
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// the same direction as 0 and 1, in between 0 and 1, with some Y axis offset.
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Values poses;
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poses.insert<Pose3>(0, Pose3(Rot3(), Point3(0, 0, 0)));
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poses.insert<Pose3>(1, Pose3(Rot3(), Point3(2, 0, 0)));
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poses.insert<Pose3>(2, Pose3(Rot3::RzRyRx(-M_PI / 2, 0, 0), Point3(2, 0, 0)));
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poses.insert<Pose3>(3, Pose3(Rot3(), Point3(1, -1, 0)));
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auto relativeTranslations = TranslationRecovery::SimulateMeasurements(
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poses, {{0, 1}, {1, 2}, {1, 3}, {3, 0}});
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// Check simulated measurements.
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for (auto& unitTranslation : relativeTranslations) {
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EXPECT(assert_equal(GetDirectionFromPoses(poses, unitTranslation),
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unitTranslation.measured()));
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}
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TranslationRecovery algorithm(relativeTranslations);
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const auto graph = algorithm.buildGraph();
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EXPECT_LONGS_EQUAL(3, graph.size());
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// Run translation recovery
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const auto result = algorithm.run(/*scale=*/4.0);
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// Check result
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EXPECT(assert_equal(Point3(0, 0, 0), result.at<Point3>(0)));
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EXPECT(assert_equal(Point3(4, 0, 0), result.at<Point3>(1)));
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EXPECT(assert_equal(Point3(4, 0, 0), result.at<Point3>(2)));
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EXPECT(assert_equal(Point3(2, -2, 0), result.at<Point3>(3)));
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}
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/* ************************************************************************* */
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int main() {
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TestResult tr;
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