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/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010, Georgia Tech Research Corporation,
* Atlanta, Georgia 30332-0415
* All Rights Reserved
* Authors: Frank Dellaert, et al. (see THANKS for the full author list)
* See LICENSE for the license information
* -------------------------------------------------------------------------- */
/**
* @file dataset.cpp
* @date Jan 22, 2010
* @author Kai Ni
* @author Luca Carlone
* @author Frank Dellaert
* @author Varun Agrawal
* @brief utility functions for loading datasets
*/
#include <gtsam/sam/BearingRangeFactor.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/slam/dataset.h>
#include <gtsam/geometry/Point3.h>
#include <gtsam/geometry/Pose2.h>
#include <gtsam/geometry/Rot3.h>
#include <gtsam/nonlinear/NonlinearFactor.h>
#include <gtsam/nonlinear/Values-inl.h>
#include <gtsam/linear/Sampler.h>
#include <gtsam/inference/FactorGraph.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/base/GenericValue.h>
#include <gtsam/base/Lie.h>
#include <gtsam/base/Matrix.h>
#include <gtsam/base/Value.h>
#include <gtsam/base/Vector.h>
#include <gtsam/base/types.h>
#include <boost/assign/list_inserter.hpp>
#include <boost/filesystem/operations.hpp>
#include <boost/filesystem/path.hpp>
#include <boost/optional.hpp>
#include <cmath>
#include <fstream>
#include <iostream>
#include <stdexcept>
using namespace std;
namespace fs = boost::filesystem;
using gtsam::symbol_shorthand::L;
#define LINESIZE 81920
namespace gtsam {
/* ************************************************************************* */
string findExampleDataFile(const string &name) {
// Search source tree and installed location
vector<string> rootsToSearch;
// Constants below are defined by CMake, see gtsam/gtsam/CMakeLists.txt
rootsToSearch.push_back(GTSAM_SOURCE_TREE_DATASET_DIR);
rootsToSearch.push_back(GTSAM_INSTALLED_DATASET_DIR);
// Search for filename as given, and with .graph and .txt extensions
vector<string> namesToSearch;
namesToSearch.push_back(name);
namesToSearch.push_back(name + ".graph");
namesToSearch.push_back(name + ".txt");
namesToSearch.push_back(name + ".out");
namesToSearch.push_back(name + ".xml");
namesToSearch.push_back(name + ".g2o");
// Find first name that exists
for (const fs::path root : rootsToSearch) {
for (const fs::path name : namesToSearch) {
if (fs::is_regular_file(root / name))
return (root / name).string();
}
}
// If we did not return already, then we did not find the file
throw invalid_argument(
"gtsam::findExampleDataFile could not find a matching "
"file in\n" GTSAM_SOURCE_TREE_DATASET_DIR
" or\n" GTSAM_INSTALLED_DATASET_DIR " named\n" +
name + ", " + name + ".g2o, " + ", " + name + ".graph, or " + name +
".txt");
}
/* ************************************************************************* */
string createRewrittenFileName(const string &name) {
// Search source tree and installed location
if (!exists(fs::path(name))) {
throw invalid_argument(
"gtsam::createRewrittenFileName could not find a matching file in\n" +
name);
}
fs::path p(name);
fs::path newpath = fs::path(p.parent_path().string()) /
fs::path(p.stem().string() + "-rewritten.txt");
return newpath.string();
}
/* ************************************************************************* */
// Type for parser functions used in parseLines below.
template <typename T>
using Parser =
std::function<boost::optional<T>(istream &is, const string &tag)>;
// Parse a file by calling the parse(is, tag) function for every line.
// The result of calling the function is ignored, so typically parse function
// works via a side effect, like adding a factor into a graph etc.
template <typename T>
static void parseLines(const string &filename, Parser<T> parse) {
ifstream is(filename.c_str());
if (!is)
throw invalid_argument("parse: can not find file " + filename);
string tag;
while (is >> tag) {
parse(is, tag); // ignore return value
is.ignore(LINESIZE, '\n');
}
}
/* ************************************************************************* */
// Parse types T into a size_t-indexed map
template <typename T>
map<size_t, T> parseToMap(const string &filename, Parser<pair<size_t, T>> parse,
size_t maxIndex) {
map<size_t, T> result;
Parser<pair<size_t, T>> emplace = [&](istream &is, const string &tag) {
if (auto t = parse(is, tag)) {
if (!maxIndex || t->first <= maxIndex)
result.emplace(*t);
}
return boost::none;
};
parseLines(filename, emplace);
return result;
}
/* ************************************************************************* */
// Parse a file and push results on a vector
template <typename T>
static vector<T> parseToVector(const string &filename, Parser<T> parse) {
vector<T> result;
Parser<T> add = [&result, parse](istream &is, const string &tag) {
if (auto t = parse(is, tag))
result.push_back(*t);
return boost::none;
};
parseLines(filename, add);
return result;
}
/* ************************************************************************* */
boost::optional<IndexedPose> parseVertexPose(istream &is, const string &tag) {
if ((tag == "VERTEX2") || (tag == "VERTEX_SE2") || (tag == "VERTEX")) {
size_t id;
double x, y, yaw;
if (!(is >> id >> x >> y >> yaw)) {
throw std::runtime_error("parseVertexPose encountered malformed line");
}
return IndexedPose(id, Pose2(x, y, yaw));
} else {
return boost::none;
}
}
template <>
GTSAM_EXPORT std::map<size_t, Pose2> parseVariables<Pose2>(
const std::string &filename, size_t maxIndex) {
return parseToMap<Pose2>(filename, parseVertexPose, maxIndex);
}
/* ************************************************************************* */
boost::optional<IndexedLandmark> parseVertexLandmark(istream &is,
const string &tag) {
if (tag == "VERTEX_XY") {
size_t id;
double x, y;
if (!(is >> id >> x >> y)) {
throw std::runtime_error(
"parseVertexLandmark encountered malformed line");
}
return IndexedLandmark(id, Point2(x, y));
} else {
return boost::none;
}
}
template <>
GTSAM_EXPORT std::map<size_t, Point2> parseVariables<Point2>(
const std::string &filename, size_t maxIndex) {
return parseToMap<Point2>(filename, parseVertexLandmark, maxIndex);
}
/* ************************************************************************* */
// Interpret noise parameters according to flags
static SharedNoiseModel createNoiseModel(
const Vector6 &v, bool smart, NoiseFormat noiseFormat,
KernelFunctionType kernelFunctionType) {
if (noiseFormat == NoiseFormatAUTO) {
// Try to guess covariance matrix layout
if (v(0) != 0.0 && v(1) == 0.0 && v(2) != 0.0 && //
v(3) != 0.0 && v(4) == 0.0 && v(5) == 0.0) {
noiseFormat = NoiseFormatGRAPH;
} else if (v(0) != 0.0 && v(1) == 0.0 && v(2) == 0.0 && //
v(3) != 0.0 && v(4) == 0.0 && v(5) != 0.0) {
noiseFormat = NoiseFormatCOV;
} else {
throw std::invalid_argument(
"load2D: unrecognized covariance matrix format in dataset file. "
"Please specify the noise format.");
}
}
// Read matrix and check that diagonal entries are non-zero
Matrix3 M;
switch (noiseFormat) {
case NoiseFormatG2O:
case NoiseFormatCOV:
// i.e., [v(0) v(1) v(2);
// v(1)' v(3) v(4);
// v(2)' v(4)' v(5) ]
if (v(0) == 0.0 || v(3) == 0.0 || v(5) == 0.0)
throw runtime_error(
"load2D::readNoiseModel looks like this is not G2O matrix order");
M << v(0), v(1), v(2), v(1), v(3), v(4), v(2), v(4), v(5);
break;
case NoiseFormatTORO:
case NoiseFormatGRAPH:
// http://www.openslam.org/toro.html
// inf_ff inf_fs inf_ss inf_rr inf_fr inf_sr
// i.e., [v(0) v(1) v(4);
// v(1)' v(2) v(5);
// v(4)' v(5)' v(3) ]
if (v(0) == 0.0 || v(2) == 0.0 || v(3) == 0.0)
throw invalid_argument(
"load2D::readNoiseModel looks like this is not TORO matrix order");
M << v(0), v(1), v(4), v(1), v(2), v(5), v(4), v(5), v(3);
break;
default:
throw runtime_error("load2D: invalid noise format");
}
// Now, create a Gaussian noise model
// The smart flag will try to detect a simpler model, e.g., unit
SharedNoiseModel model;
switch (noiseFormat) {
case NoiseFormatG2O:
case NoiseFormatTORO:
// In both cases, what is stored in file is the information matrix
model = noiseModel::Gaussian::Information(M, smart);
break;
case NoiseFormatGRAPH:
case NoiseFormatCOV:
// These cases expect covariance matrix
model = noiseModel::Gaussian::Covariance(M, smart);
break;
default:
throw invalid_argument("load2D: invalid noise format");
}
switch (kernelFunctionType) {
case KernelFunctionTypeNONE:
return model;
break;
case KernelFunctionTypeHUBER:
return noiseModel::Robust::Create(
noiseModel::mEstimator::Huber::Create(1.345), model);
break;
case KernelFunctionTypeTUKEY:
return noiseModel::Robust::Create(
noiseModel::mEstimator::Tukey::Create(4.6851), model);
break;
default:
throw invalid_argument("load2D: invalid kernel function type");
}
}
/* ************************************************************************* */
boost::optional<IndexedEdge> parseEdge(istream &is, const string &tag) {
if ((tag == "EDGE2") || (tag == "EDGE") || (tag == "EDGE_SE2") ||
(tag == "ODOMETRY")) {
size_t id1, id2;
double x, y, yaw;
if (!(is >> id1 >> id2 >> x >> y >> yaw)) {
throw std::runtime_error("parseEdge encountered malformed line");
}
return IndexedEdge({id1, id2}, Pose2(x, y, yaw));
} else {
return boost::none;
}
}
/* ************************************************************************* */
// Measurement parsers are implemented as a functor, as they have several
// options to filter and create the measurement model.
template <typename T> struct ParseMeasurement;
/* ************************************************************************* */
// Converting from Measurement to BetweenFactor is generic
template <typename T> struct ParseFactor : ParseMeasurement<T> {
ParseFactor(const ParseMeasurement<T> &parent)
: ParseMeasurement<T>(parent) {}
// We parse a measurement then convert
typename boost::optional<typename BetweenFactor<T>::shared_ptr>
operator()(istream &is, const string &tag) {
if (auto m = ParseMeasurement<T>::operator()(is, tag))
return boost::make_shared<BetweenFactor<T>>(
m->key1(), m->key2(), m->measured(), m->noiseModel());
else
return boost::none;
}
};
/* ************************************************************************* */
// Pose2 measurement parser
template <> struct ParseMeasurement<Pose2> {
// The arguments
boost::shared_ptr<Sampler> sampler;
size_t maxIndex;
// For noise model creation
bool smart;
NoiseFormat noiseFormat;
KernelFunctionType kernelFunctionType;
// If this is not null, will use instead of parsed model:
SharedNoiseModel model;
// The actual parser
boost::optional<BinaryMeasurement<Pose2>> operator()(istream &is,
const string &tag) {
auto edge = parseEdge(is, tag);
if (!edge)
return boost::none;
// parse noise model
Vector6 v;
is >> v(0) >> v(1) >> v(2) >> v(3) >> v(4) >> v(5);
// optional filter
size_t id1, id2;
tie(id1, id2) = edge->first;
if (maxIndex && (id1 > maxIndex || id2 > maxIndex))
return boost::none;
// Get pose and optionally add noise
Pose2 &pose = edge->second;
if (sampler)
pose = pose.retract(sampler->sample());
// emplace measurement
auto modelFromFile =
createNoiseModel(v, smart, noiseFormat, kernelFunctionType);
return BinaryMeasurement<Pose2>(id1, id2, pose,
model ? model : modelFromFile);
}
};
/* ************************************************************************* */
// Create a sampler to corrupt a measurement
boost::shared_ptr<Sampler> createSampler(const SharedNoiseModel &model) {
auto noise = boost::dynamic_pointer_cast<noiseModel::Diagonal>(model);
if (!noise)
throw invalid_argument("gtsam::load: invalid noise model for adding noise"
"(current version assumes diagonal noise model)!");
return boost::shared_ptr<Sampler>(new Sampler(noise));
}
/* ************************************************************************* */
// Implementation of parseMeasurements for Pose2
template <>
GTSAM_EXPORT
std::vector<BinaryMeasurement<Pose2>>
parseMeasurements(const std::string &filename,
const noiseModel::Diagonal::shared_ptr &model,
size_t maxIndex) {
ParseMeasurement<Pose2> parse{model ? createSampler(model) : nullptr,
maxIndex, true, NoiseFormatAUTO,
KernelFunctionTypeNONE, nullptr};
return parseToVector<BinaryMeasurement<Pose2>>(filename, parse);
}
/* ************************************************************************* */
// Implementation of parseMeasurements for Rot2, which converts from Pose2
// Extract Rot2 measurement from Pose2 measurement
static BinaryMeasurement<Rot2> convert(const BinaryMeasurement<Pose2> &p) {
auto gaussian =
boost::dynamic_pointer_cast<noiseModel::Gaussian>(p.noiseModel());
if (!gaussian)
throw std::invalid_argument(
"parseMeasurements<Rot2> can only convert Pose2 measurements "
"with Gaussian noise models.");
const Matrix3 M = gaussian->covariance();
return BinaryMeasurement<Rot2>(p.key1(), p.key2(), p.measured().rotation(),
noiseModel::Isotropic::Variance(1, M(2, 2)));
}
template <>
GTSAM_EXPORT
std::vector<BinaryMeasurement<Rot2>>
parseMeasurements(const std::string &filename,
const noiseModel::Diagonal::shared_ptr &model,
size_t maxIndex) {
auto poses = parseMeasurements<Pose2>(filename, model, maxIndex);
std::vector<BinaryMeasurement<Rot2>> result;
result.reserve(poses.size());
for (const auto &p : poses)
result.push_back(convert(p));
return result;
}
/* ************************************************************************* */
// Implementation of parseFactors for Pose2
template <>
GTSAM_EXPORT
std::vector<BetweenFactor<Pose2>::shared_ptr>
parseFactors<Pose2>(const std::string &filename,
const noiseModel::Diagonal::shared_ptr &model,
size_t maxIndex) {
ParseFactor<Pose2> parse({model ? createSampler(model) : nullptr, maxIndex,
true, NoiseFormatAUTO, KernelFunctionTypeNONE,
nullptr});
return parseToVector<BetweenFactor<Pose2>::shared_ptr>(filename, parse);
}
/* ************************************************************************* */
using BearingRange2D = BearingRange<Pose2, Point2>;
template <> struct ParseMeasurement<BearingRange2D> {
// arguments
size_t maxIndex;
// The actual parser
boost::optional<BinaryMeasurement<BearingRange2D>>
operator()(istream &is, const string &tag) {
if (tag != "BR" && tag != "LANDMARK")
return boost::none;
size_t id1, id2;
is >> id1 >> id2;
double bearing, range, bearing_std, range_std;
if (tag == "BR") {
is >> bearing >> range >> bearing_std >> range_std;
}
// A landmark measurement, converted to bearing-range
if (tag == "LANDMARK") {
double lmx, lmy;
double v1, v2, v3;
is >> lmx >> lmy >> v1 >> v2 >> v3;
// Convert x,y to bearing,range
bearing = atan2(lmy, lmx);
range = sqrt(lmx * lmx + lmy * lmy);
// In our experience, the x-y covariance on landmark sightings is not
// very good, so assume it describes the uncertainty at a range of 10m,
// and convert that to bearing/range uncertainty.
if (std::abs(v1 - v3) < 1e-4) {
bearing_std = sqrt(v1 / 10.0);
range_std = sqrt(v1);
} else {
// TODO(frank): we are ignoring the non-uniform covariance
bearing_std = 1;
range_std = 1;
}
}
// optional filter
if (maxIndex && id1 > maxIndex)
return boost::none;
// Create noise model
auto measurementNoise = noiseModel::Diagonal::Sigmas(
(Vector(2) << bearing_std, range_std).finished());
return BinaryMeasurement<BearingRange2D>(
id1, L(id2), BearingRange2D(bearing, range), measurementNoise);
}
};
/* ************************************************************************* */
GraphAndValues load2D(const string &filename, SharedNoiseModel model,
size_t maxIndex, bool addNoise, bool smart,
NoiseFormat noiseFormat,
KernelFunctionType kernelFunctionType) {
// Single pass for poses and landmarks.
auto initial = boost::make_shared<Values>();
Parser<int> insert = [maxIndex, &initial](istream &is, const string &tag) {
if (auto indexedPose = parseVertexPose(is, tag)) {
if (!maxIndex || indexedPose->first <= maxIndex)
initial->insert(indexedPose->first, indexedPose->second);
} else if (auto indexedLandmark = parseVertexLandmark(is, tag)) {
if (!maxIndex || indexedLandmark->first <= maxIndex)
initial->insert(L(indexedLandmark->first), indexedLandmark->second);
}
return 0;
};
parseLines(filename, insert);
// Single pass for Pose2 and bearing-range factors.
auto graph = boost::make_shared<NonlinearFactorGraph>();
// Instantiate factor parser
ParseFactor<Pose2> parseBetweenFactor(
{addNoise ? createSampler(model) : nullptr, maxIndex, smart, noiseFormat,
kernelFunctionType, model});
// Instantiate bearing-range parser
ParseMeasurement<BearingRange2D> parseBearingRange{maxIndex};
// Combine in a single parser that adds factors to `graph`, but also inserts
// new variables into `initial` when needed.
Parser<int> parse = [&](istream &is, const string &tag) {
if (auto f = parseBetweenFactor(is, tag)) {
graph->push_back(*f);
// Insert vertices if pure odometry file
Key key1 = (*f)->key1(), key2 = (*f)->key2();
if (!initial->exists(key1))
initial->insert(key1, Pose2());
if (!initial->exists(key2))
initial->insert(key2, initial->at<Pose2>(key1) * (*f)->measured());
} else if (auto m = parseBearingRange(is, tag)) {
Key key1 = m->key1(), key2 = m->key2();
BearingRange2D br = m->measured();
graph->emplace_shared<BearingRangeFactor<Pose2, Point2>>(key1, key2, br,
m->noiseModel());
// Insert poses or points if they do not exist yet
if (!initial->exists(key1))
initial->insert(key1, Pose2());
if (!initial->exists(key2)) {
Pose2 pose = initial->at<Pose2>(key1);
Point2 local = br.bearing() * Point2(br.range(), 0);
Point2 global = pose.transformFrom(local);
initial->insert(key2, global);
}
}
return 0;
};
parseLines(filename, parse);
return make_pair(graph, initial);
}
/* ************************************************************************* */
GraphAndValues load2D(pair<string, SharedNoiseModel> dataset, size_t maxIndex,
bool addNoise, bool smart, NoiseFormat noiseFormat,
KernelFunctionType kernelFunctionType) {
return load2D(dataset.first, dataset.second, maxIndex, addNoise, smart,
noiseFormat, kernelFunctionType);
}
/* ************************************************************************* */
GraphAndValues load2D_robust(const string &filename,
const noiseModel::Base::shared_ptr &model,
size_t maxIndex) {
return load2D(filename, model, maxIndex);
}
/* ************************************************************************* */
void save2D(const NonlinearFactorGraph &graph, const Values &config,
const noiseModel::Diagonal::shared_ptr model,
const string &filename) {
fstream stream(filename.c_str(), fstream::out);
// save poses
for (const auto key_value : config) {
const Pose2 &pose = key_value.value.cast<Pose2>();
stream << "VERTEX2 " << key_value.key << " " << pose.x() << " " << pose.y()
<< " " << pose.theta() << endl;
}
// save edges
// TODO(frank): why don't we use model in factor?
Matrix3 R = model->R();
Matrix3 RR = R.transpose() * R;
for (auto f : graph) {
auto factor = boost::dynamic_pointer_cast<BetweenFactor<Pose2>>(f);
if (!factor)
continue;
const Pose2 pose = factor->measured().inverse();
stream << "EDGE2 " << factor->key2() << " " << factor->key1() << " "
<< pose.x() << " " << pose.y() << " " << pose.theta() << " "
<< RR(0, 0) << " " << RR(0, 1) << " " << RR(1, 1) << " " << RR(2, 2)
<< " " << RR(0, 2) << " " << RR(1, 2) << endl;
}
stream.close();
}
/* ************************************************************************* */
GraphAndValues readG2o(const string &g2oFile, const bool is3D,
KernelFunctionType kernelFunctionType) {
if (is3D) {
return load3D(g2oFile);
} else {
// just call load2D
size_t maxIndex = 0;
bool addNoise = false;
bool smart = true;
return load2D(g2oFile, SharedNoiseModel(), maxIndex, addNoise, smart,
NoiseFormatG2O, kernelFunctionType);
}
}
/* ************************************************************************* */
void writeG2o(const NonlinearFactorGraph &graph, const Values &estimate,
const string &filename) {
fstream stream(filename.c_str(), fstream::out);
// Use a lambda here to more easily modify behavior in future.
auto index = [](gtsam::Key key) { return Symbol(key).index(); };
// save 2D poses
for (const auto key_value : estimate) {
auto p = dynamic_cast<const GenericValue<Pose2> *>(&key_value.value);
if (!p)
continue;
const Pose2 &pose = p->value();
stream << "VERTEX_SE2 " << index(key_value.key) << " " << pose.x() << " "
<< pose.y() << " " << pose.theta() << endl;
}
// save 3D poses
for (const auto key_value : estimate) {
auto p = dynamic_cast<const GenericValue<Pose3> *>(&key_value.value);
if (!p)
continue;
const Pose3 &pose = p->value();
const Point3 t = pose.translation();
const auto q = pose.rotation().toQuaternion();
stream << "VERTEX_SE3:QUAT " << index(key_value.key) << " " << t.x() << " "
<< t.y() << " " << t.z() << " " << q.x() << " " << q.y() << " "
<< q.z() << " " << q.w() << endl;
}
// save 2D landmarks
for (const auto key_value : estimate) {
auto p = dynamic_cast<const GenericValue<Point2> *>(&key_value.value);
if (!p)
continue;
const Point2 &point = p->value();
stream << "VERTEX_XY " << index(key_value.key) << " " << point.x() << " "
<< point.y() << endl;
}
// save 3D landmarks
for (const auto key_value : estimate) {
auto p = dynamic_cast<const GenericValue<Point3> *>(&key_value.value);
if (!p)
continue;
const Point3 &point = p->value();
stream << "VERTEX_TRACKXYZ " << index(key_value.key) << " " << point.x()
<< " " << point.y() << " " << point.z() << endl;
}
// save edges (2D or 3D)
for (const auto &factor_ : graph) {
auto factor = boost::dynamic_pointer_cast<BetweenFactor<Pose2>>(factor_);
if (factor) {
SharedNoiseModel model = factor->noiseModel();
auto gaussianModel =
boost::dynamic_pointer_cast<noiseModel::Gaussian>(model);
if (!gaussianModel) {
model->print("model\n");
throw invalid_argument("writeG2o: invalid noise model!");
}
Matrix3 Info = gaussianModel->R().transpose() * gaussianModel->R();
Pose2 pose = factor->measured(); //.inverse();
stream << "EDGE_SE2 " << index(factor->key1()) << " "
<< index(factor->key2()) << " " << pose.x() << " " << pose.y()
<< " " << pose.theta();
for (size_t i = 0; i < 3; i++) {
for (size_t j = i; j < 3; j++) {
stream << " " << Info(i, j);
}
}
stream << endl;
}
auto factor3D = boost::dynamic_pointer_cast<BetweenFactor<Pose3>>(factor_);
if (factor3D) {
SharedNoiseModel model = factor3D->noiseModel();
boost::shared_ptr<noiseModel::Gaussian> gaussianModel =
boost::dynamic_pointer_cast<noiseModel::Gaussian>(model);
if (!gaussianModel) {
model->print("model\n");
throw invalid_argument("writeG2o: invalid noise model!");
}
Matrix6 Info = gaussianModel->R().transpose() * gaussianModel->R();
const Pose3 pose3D = factor3D->measured();
const Point3 p = pose3D.translation();
const auto q = pose3D.rotation().toQuaternion();
stream << "EDGE_SE3:QUAT " << index(factor3D->key1()) << " "
<< index(factor3D->key2()) << " " << p.x() << " " << p.y() << " "
<< p.z() << " " << q.x() << " " << q.y() << " " << q.z() << " "
<< q.w();
// g2o's EDGE_SE3:QUAT stores information/precision of Pose3 in t,R order, unlike GTSAM:
Matrix6 InfoG2o = I_6x6;
InfoG2o.block<3, 3>(0, 0) = Info.block<3, 3>(3, 3); // cov translation
InfoG2o.block<3, 3>(3, 3) = Info.block<3, 3>(0, 0); // cov rotation
InfoG2o.block<3, 3>(0, 3) = Info.block<3, 3>(3, 0); // off diagonal R,t -> t,R
InfoG2o.block<3, 3>(3, 0) = Info.block<3, 3>(0, 3); // off diagonal t,R -> R,t
for (size_t i = 0; i < 6; i++) {
for (size_t j = i; j < 6; j++) {
stream << " " << InfoG2o(i, j);
}
}
stream << endl;
}
}
stream.close();
}
/* ************************************************************************* */
// parse quaternion in x,y,z,w order, and normalize to unit length
istream &operator>>(istream &is, Quaternion &q) {
double x, y, z, w;
is >> x >> y >> z >> w;
const double norm = sqrt(w * w + x * x + y * y + z * z), f = 1.0 / norm;
q = Quaternion(f * w, f * x, f * y, f * z);
return is;
}
/* ************************************************************************* */
// parse Rot3 from roll, pitch, yaw
istream &operator>>(istream &is, Rot3 &R) {
double yaw, pitch, roll;
is >> roll >> pitch >> yaw; // notice order !
R = Rot3::Ypr(yaw, pitch, roll);
return is;
}
/* ************************************************************************* */
boost::optional<pair<size_t, Pose3>> parseVertexPose3(istream &is,
const string &tag) {
if (tag == "VERTEX3") {
size_t id;
double x, y, z;
Rot3 R;
is >> id >> x >> y >> z >> R;
return make_pair(id, Pose3(R, {x, y, z}));
} else if (tag == "VERTEX_SE3:QUAT") {
size_t id;
double x, y, z;
Quaternion q;
is >> id >> x >> y >> z >> q;
return make_pair(id, Pose3(q, {x, y, z}));
} else
return boost::none;
}
template <>
GTSAM_EXPORT std::map<size_t, Pose3> parseVariables<Pose3>(
const std::string &filename, size_t maxIndex) {
return parseToMap<Pose3>(filename, parseVertexPose3, maxIndex);
}
/* ************************************************************************* */
boost::optional<pair<size_t, Point3>> parseVertexPoint3(istream &is,
const string &tag) {
if (tag == "VERTEX_TRACKXYZ") {
size_t id;
double x, y, z;
is >> id >> x >> y >> z;
return make_pair(id, Point3(x, y, z));
} else
return boost::none;
}
template <>
GTSAM_EXPORT std::map<size_t, Point3> parseVariables<Point3>(
const std::string &filename, size_t maxIndex) {
return parseToMap<Point3>(filename, parseVertexPoint3, maxIndex);
}
/* ************************************************************************* */
// Parse a symmetric covariance matrix (onlyupper-triangular part is stored)
istream &operator>>(istream &is, Matrix6 &m) {
for (size_t i = 0; i < 6; i++)
for (size_t j = i; j < 6; j++) {
is >> m(i, j);
m(j, i) = m(i, j);
}
return is;
}
/* ************************************************************************* */
// Pose3 measurement parser
template <> struct ParseMeasurement<Pose3> {
// The arguments
boost::shared_ptr<Sampler> sampler;
size_t maxIndex;
// The actual parser
boost::optional<BinaryMeasurement<Pose3>> operator()(istream &is,
const string &tag) {
if (tag != "EDGE3" && tag != "EDGE_SE3:QUAT")
return boost::none;
// parse ids and optionally filter
size_t id1, id2;
is >> id1 >> id2;
if (maxIndex && (id1 > maxIndex || id2 > maxIndex))
return boost::none;
Matrix6 m;
if (tag == "EDGE3") {
double x, y, z;
Rot3 R;
is >> x >> y >> z >> R >> m;
Pose3 T12(R, {x, y, z});
// optionally add noise
if (sampler)
T12 = T12.retract(sampler->sample());
return BinaryMeasurement<Pose3>(id1, id2, T12,
noiseModel::Gaussian::Information(m));
} else if (tag == "EDGE_SE3:QUAT") {
double x, y, z;
Quaternion q;
is >> x >> y >> z >> q >> m;
Pose3 T12(q, {x, y, z});
// optionally add noise
if (sampler)
T12 = T12.retract(sampler->sample());
// g2o's EDGE_SE3:QUAT stores information/precision of Pose3 in t,R order, unlike GTSAM:
Matrix6 mgtsam;
mgtsam.block<3, 3>(0, 0) = m.block<3, 3>(3, 3); // info rotation
mgtsam.block<3, 3>(3, 3) = m.block<3, 3>(0, 0); // info translation
mgtsam.block<3, 3>(3, 0) = m.block<3, 3>(0, 3); // off diagonal g2o t,R -> GTSAM R,t
mgtsam.block<3, 3>(0, 3) = m.block<3, 3>(3, 0); // off diagonal g2o R,t -> GTSAM t,R
SharedNoiseModel model = noiseModel::Gaussian::Information(mgtsam);
return BinaryMeasurement<Pose3>(
id1, id2, T12, noiseModel::Gaussian::Information(mgtsam));
} else
return boost::none;
}
};
/* ************************************************************************* */
// Implementation of parseMeasurements for Pose3
template <>
GTSAM_EXPORT
std::vector<BinaryMeasurement<Pose3>>
parseMeasurements(const std::string &filename,
const noiseModel::Diagonal::shared_ptr &model,
size_t maxIndex) {
ParseMeasurement<Pose3> parse{model ? createSampler(model) : nullptr,
maxIndex};
return parseToVector<BinaryMeasurement<Pose3>>(filename, parse);
}
/* ************************************************************************* */
// Implementation of parseMeasurements for Rot3, which converts from Pose3
// Extract Rot3 measurement from Pose3 measurement
static BinaryMeasurement<Rot3> convert(const BinaryMeasurement<Pose3> &p) {
auto gaussian =
boost::dynamic_pointer_cast<noiseModel::Gaussian>(p.noiseModel());
if (!gaussian)
throw std::invalid_argument(
"parseMeasurements<Rot3> can only convert Pose3 measurements "
"with Gaussian noise models.");
const Matrix6 M = gaussian->covariance();
return BinaryMeasurement<Rot3>(
p.key1(), p.key2(), p.measured().rotation(),
noiseModel::Gaussian::Covariance(M.block<3, 3>(0, 0), true));
}
template <>
GTSAM_EXPORT
std::vector<BinaryMeasurement<Rot3>>
parseMeasurements(const std::string &filename,
const noiseModel::Diagonal::shared_ptr &model,
size_t maxIndex) {
auto poses = parseMeasurements<Pose3>(filename, model, maxIndex);
std::vector<BinaryMeasurement<Rot3>> result;
result.reserve(poses.size());
for (const auto &p : poses)
result.push_back(convert(p));
return result;
}
/* ************************************************************************* */
// Implementation of parseFactors for Pose3
template <>
GTSAM_EXPORT
std::vector<BetweenFactor<Pose3>::shared_ptr>
parseFactors<Pose3>(const std::string &filename,
const noiseModel::Diagonal::shared_ptr &model,
size_t maxIndex) {
ParseFactor<Pose3> parse({model ? createSampler(model) : nullptr, maxIndex});
return parseToVector<BetweenFactor<Pose3>::shared_ptr>(filename, parse);
}
/* ************************************************************************* */
GraphAndValues load3D(const string &filename) {
auto graph = boost::make_shared<NonlinearFactorGraph>();
auto initial = boost::make_shared<Values>();
// Instantiate factor parser. maxIndex is always zero for load3D.
ParseFactor<Pose3> parseFactor({nullptr, 0});
// Single pass for variables and factors. Unlike 2D version, does *not* insert
// variables into `initial` if referenced but not present.
Parser<int> parse = [&](istream &is, const string &tag) {
if (auto indexedPose = parseVertexPose3(is, tag)) {
initial->insert(indexedPose->first, indexedPose->second);
} else if (auto indexedLandmark = parseVertexPoint3(is, tag)) {
initial->insert(L(indexedLandmark->first), indexedLandmark->second);
} else if (auto factor = parseFactor(is, tag)) {
graph->push_back(*factor);
}
return 0;
};
parseLines(filename, parse);
return make_pair(graph, initial);
}
// Wrapper-friendly versions of parseFactors<Pose2> and parseFactors<Pose2>
BetweenFactorPose2s
parse2DFactors(const std::string &filename,
const noiseModel::Diagonal::shared_ptr &model, size_t maxIndex) {
return parseFactors<Pose2>(filename, model, maxIndex);
}
BetweenFactorPose3s
parse3DFactors(const std::string &filename,
const noiseModel::Diagonal::shared_ptr &model, size_t maxIndex) {
return parseFactors<Pose3>(filename, model, maxIndex);
}
#ifdef GTSAM_ALLOW_DEPRECATED_SINCE_V42
std::map<size_t, Pose3> GTSAM_DEPRECATED
parse3DPoses(const std::string &filename, size_t maxIndex) {
return parseVariables<Pose3>(filename, maxIndex);
}
std::map<size_t, Point3> GTSAM_DEPRECATED
parse3DLandmarks(const std::string &filename, size_t maxIndex) {
return parseVariables<Point3>(filename, maxIndex);
}
#endif
} // namespace gtsam
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