12345qiupeng
/
gtsam
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Merge branch 'develop' of github.com:borglab/gtsam into fix/warning_in_smart_factor 

* 'develop' of github.com:borglab/gtsam: (65 commits)
  type in test hidden by duplicate test values
  auto and reserve fewer
  replace sparseJacobian with "fast" version
  disambiguate double template >>
  fix comment and remove whitespace diff
  remove InPlace jacobian from .h file
  remove unnecessary function overloads and typedefs
  use standard function to check for empty string
  fix bug in Pose2 print
  formatting
  revert Matrix.h
  remove templating while maintaining efficiency Templating still used in cpp file for generic-ness, but not exposed anymore
  move SparseMatrixBoostTriplets typedef to gfg
  add known issues section with info about march=native
  Revert "upgrade minimum required Boost version to 1.67."
  upgrade minimum required Boost version to 1.67.
  populate sparse matrix with `insert` rather than `setFromTriplets` About 5% speed improvement.
  eliminate copy/pasta from SparseEigen with generic version of sparseJacobian
  more generic sparseJacobianInPlace function
  add generic optional parameters to sparseJacobian Also, the unit test changed due to a 0 entry that was previously wrongly included in the b-column of the sparse representation.
  ...
release/4.3a0
Toni 2021-01-28 22:49:06 -05:00
parent 241d82dbf7 ca44312bc9
commit c073473f5b
32 changed files with 3899 additions and 97 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

12
INSTALL.md
View File

@ -41,11 +41,6 @@ $ make install
- MacOS 10.6 - 10.14
- Windows 7, 8, 8.1, 10
Known issues:
- MSVC 2013 is not yet supported because it cannot build the serialization module
of Boost 1.55 (or earlier).
2. GTSAM makes extensive use of debug assertions, and we highly recommend you work
in Debug mode while developing (enabled by default). Likewise, it is imperative
that you switch to release mode when running finished code and for timing. GTSAM
@ -70,6 +65,13 @@ execute commands as follows for an out-of-source build:
This will build the library and unit tests, run all of the unit tests,
and then install the library itself.
## Known Issues
- When using `GTSAM_BUILD_WITH_MARCH_NATIVE=ON`, you may encounter issues in running tests which we are still investigating:
- Use of a version of GCC < 7.5 results in an "Indeterminant Linear System" error for `testSmartProjectionFactor`.
- Use of Boost version < 1.67 with clang will give a segfault for mulitple test cases.
- MSVC 2013 is not yet supported because it cannot build the serialization module of Boost 1.55 (or earlier).
# Windows Installation
This section details how to build a GTSAM `.sln` file using Visual Studio.

2636
examples/Data/quadraped_imu_data.csv Normal file
View File

File diff suppressed because it is too large Load Diff

2
gtsam/base/Matrix.cpp
View File

@ -153,7 +153,7 @@ const Eigen::IOFormat& matlabFormat() {
/* ************************************************************************* */
//3 argument call
void print(const Matrix& A, const string &s, ostream& stream) {
cout << s << A.format(matlabFormat()) << endl;
stream << s << A.format(matlabFormat()) << endl;
}
/* ************************************************************************* */

8
gtsam/base/Testable.h
View File

@ -33,9 +33,9 @@
#pragma once
#include <boost/shared_ptr.hpp>
#include <boost/concept_check.hpp>
#include <stdio.h>
#include <boost/shared_ptr.hpp>
#include <iostream>
#include <string>
#define GTSAM_PRINT(x)((x).print(#x))
@ -72,10 +72,10 @@ namespace gtsam {
}; // \ Testable
inline void print(float v, const std::string& s = "") {
printf("%s%f\n",s.c_str(),v);
std::cout << (s.empty() ? s : s + " ") << v << std::endl;
}
inline void print(double v, const std::string& s = "") {
printf("%s%lf\n",s.c_str(),v);
std::cout << (s.empty() ? s : s + " ") << v << std::endl;
}
/** Call equal on the object */

9
gtsam/base/TestableAssertions.h
View File

@ -372,16 +372,19 @@ bool assert_stdout_equal(const std::string& expected, const V& actual) {
/**
* Capture print function output and compare against string.
*
* @param s: Optional string to pass to the print() method.
*/
template<class V>
bool assert_print_equal(const std::string& expected, const V& actual) {
template <class V>
bool assert_print_equal(const std::string& expected, const V& actual,
const std::string& s = "") {
// Redirect output to buffer so we can compare
std::stringstream buffer;
// Save the original output stream so we can reset later
std::streambuf* old = std::cout.rdbuf(buffer.rdbuf());
// We test against actual std::cout for faithful reproduction
actual.print();
actual.print(s);
// Get output string and reset stdout
std::string actual_ = buffer.str();

6
gtsam/base/tests/testNumericalDerivative.cpp
View File

@ -30,10 +30,10 @@ double f(const Vector2& x) {
/* ************************************************************************* */
//
TEST(testNumericalDerivative, numericalGradient) {
Vector2 x(1, 1);
Vector2 x(1, 1.1);
Vector expected(2);
expected << cos(x(1)), -sin(x(0));
expected << cos(x(0)), -sin(x(1));
Vector actual = numericalGradient<Vector2>(f, x);
@ -42,7 +42,7 @@ TEST(testNumericalDerivative, numericalGradient) {
/* ************************************************************************* */
TEST(testNumericalDerivative, numericalHessian) {
Vector2 x(1, 1);
Vector2 x(1, 1.1);
Matrix expected(2, 2);
expected << -sin(x(0)), 0.0, 0.0, -cos(x(1));

8
gtsam/geometry/Pose2.cpp
View File

@ -48,7 +48,13 @@ Matrix3 Pose2::matrix() const {
/* ************************************************************************* */
void Pose2::print(const string& s) const {
cout << s << "(" << t_.x() << ", " << t_.y() << ", " << r_.theta() << ")" << endl;
std::cout << (s.empty() ? s : s + " ") << *this << std::endl;
}
/* ************************************************************************* */
std::ostream &operator<<(std::ostream &os, const Pose2& pose) {
os << "(" << pose.x() << ", " << pose.y() << ", " << pose.theta() << ")";
return os;
}
/* ************************************************************************* */

4
gtsam/geometry/Pose2.h
View File

@ -287,6 +287,10 @@ public:
*/
static std::pair<size_t, size_t> rotationInterval() { return std::make_pair(2, 2); }
/// Output stream operator
GTSAM_EXPORT
friend std::ostream &operator<<(std::ostream &os, const Pose2& p);
/// @}
private:

33
gtsam/geometry/tests/testPose2.cpp
View File

@ -14,16 +14,17 @@
* @brief Unit tests for Pose2 class
*/
#include <gtsam/geometry/Pose2.h>
#include <gtsam/geometry/Point2.h>
#include <gtsam/geometry/Rot2.h>
#include <gtsam/base/Testable.h>
#include <gtsam/base/testLie.h>
#include <gtsam/base/lieProxies.h>
#include <CppUnitLite/TestHarness.h>
#include <gtsam/base/Testable.h>
#include <gtsam/base/TestableAssertions.h>
#include <gtsam/base/lieProxies.h>
#include <gtsam/base/testLie.h>
#include <gtsam/geometry/Point2.h>
#include <gtsam/geometry/Pose2.h>
#include <gtsam/geometry/Rot2.h>
#include <boost/assign/std/vector.hpp> // for operator +=
#include <boost/optional.hpp>
#include <boost/assign/std/vector.hpp> // for operator +=
#include <cmath>
#include <iostream>
@ -910,6 +911,22 @@ TEST(Pose2 , TransformCovariance3) {
}
}
/* ************************************************************************* */
TEST(Pose2, Print) {
Pose2 pose(Rot2::identity(), Point2(1, 2));
// Generate the expected output
string s = "Planar Pose";
string expected_stdout = "(1, 2, 0)";
string expected1 = expected_stdout + "\n";
string expected2 = s + " " + expected1;
EXPECT(assert_stdout_equal(expected_stdout, pose));
EXPECT(assert_print_equal(expected1, pose));
EXPECT(assert_print_equal(expected2, pose, s));
}
/* ************************************************************************* */
int main() {
TestResult tr;

2
gtsam/gtsam.i
View File

@ -2623,6 +2623,8 @@ virtual class BearingRangeFactor : gtsam::NoiseModelFactor {
const BEARING& measuredBearing, const RANGE& measuredRange,
const gtsam::noiseModel::Base* noiseModel);
BearingRange<POSE, POINT, BEARING, RANGE> measured() const;
// enabling serialization functionality
void serialize() const;
};

176
gtsam/linear/AcceleratedPowerMethod.h Normal file
View File

@ -0,0 +1,176 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010-2019, 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 AcceleratedPowerMethod.h
* @date Sept 2020
* @author Jing Wu
* @brief accelerated power method for fast eigenvalue and eigenvector
* computation
*/
#pragma once
#include <gtsam/linear/PowerMethod.h>
namespace gtsam {
using Sparse = Eigen::SparseMatrix<double>;
/**
* \brief Compute maximum Eigenpair with accelerated power method
*
* References :
* 1) G. Golub and C. V. Loan, Matrix Computations, 3rd ed. Baltimore, Johns
* Hopkins University Press, 1996, pp.405-411
* 2) Rosen, D. and Carlone, L., 2017, September. Computational
* enhancements for certifiably correct SLAM. In Proceedings of the
* International Conference on Intelligent Robots and Systems.
* 3) Yulun Tian and Kasra Khosoussi and David M. Rosen and Jonathan P. How,
* 2020, Aug, Distributed Certifiably Correct Pose-Graph Optimization, Arxiv
* 4) C. de Sa, B. He, I. Mitliagkas, C. Ré, and P. Xu, “Accelerated
* stochastic power iteration,” in Proc. Mach. Learn. Res., no. 84, 2018, pp.
* 58–67
*
* It performs the following iteration: \f$ x_{k+1} = A * x_k - \beta *
* x_{k-1} \f$ where A is the aim matrix we want to get eigenpair of, x is the
* Ritz vector
*
* Template argument Operator just needs multiplication operator
*
*/
template <class Operator>
class AcceleratedPowerMethod : public PowerMethod<Operator> {
double beta_ = 0; // a Polyak momentum term
Vector previousVector_; // store previous vector
public:
/**
* Constructor from aim matrix A (given as Matrix or Sparse), optional intial
* vector as ritzVector
*/
explicit AcceleratedPowerMethod(
const Operator &A, const boost::optional<Vector> initial = boost::none,
double initialBeta = 0.0)
: PowerMethod<Operator>(A, initial) {
// initialize Ritz eigen vector and previous vector
this->ritzVector_ = initial ? initial.get() : Vector::Random(this->dim_);
this->ritzVector_.normalize();
previousVector_ = Vector::Zero(this->dim_);
// initialize beta_
beta_ = initialBeta;
}
/**
* Run accelerated power iteration to get ritzVector with beta and previous
* two ritzVector x0 and x00, and return y = (A * x0 - \beta * x00) / || A * x0
* - \beta * x00 ||
*/
Vector acceleratedPowerIteration (const Vector &x1, const Vector &x0,
const double beta) const {
Vector y = this->A_ * x1 - beta * x0;
y.normalize();
return y;
}
/**
* Run accelerated power iteration to get ritzVector with beta and previous
* two ritzVector x0 and x00, and return y = (A * x0 - \beta * x00) / || A * x0
* - \beta * x00 ||
*/
Vector acceleratedPowerIteration () const {
Vector y = acceleratedPowerIteration(this->ritzVector_, previousVector_, beta_);
return y;
}
/**
* Tuning the momentum beta using the Best Heavy Ball algorithm in Ref(3), T
* is the iteration time to find beta with largest Rayleigh quotient
*/
double estimateBeta(const size_t T = 10) const {
// set initial estimation of maxBeta
Vector initVector = this->ritzVector_;
const double up = initVector.dot( this->A_ * initVector );
const double down = initVector.dot(initVector);
const double mu = up / down;
double maxBeta = mu * mu / 4;
size_t maxIndex;
std::vector<double> betas;
Matrix R = Matrix::Zero(this->dim_, 10);
// run T times of iteration to find the beta that has the largest Rayleigh quotient
for (size_t t = 0; t < T; t++) {
// after each t iteration, reset the betas with the current maxBeta
betas = {2 / 3 * maxBeta, 0.99 * maxBeta, maxBeta, 1.01 * maxBeta,
1.5 * maxBeta};
// iterate through every beta value
for (size_t k = 0; k < betas.size(); ++k) {
// initialize x0 and x00 in each iteration of each beta
Vector x0 = initVector;
Vector x00 = Vector::Zero(this->dim_);
// run 10 steps of accelerated power iteration with this beta
for (size_t j = 1; j < 10; j++) {
if (j < 2) {
R.col(0) = acceleratedPowerIteration(x0, x00, betas[k]);
R.col(1) = acceleratedPowerIteration(R.col(0), x0, betas[k]);
} else {
R.col(j) = acceleratedPowerIteration(R.col(j - 1), R.col(j - 2),
betas[k]);
}
}
// compute the Rayleigh quotient for the randomly sampled vector after
// 10 steps of power accelerated iteration
const Vector x = R.col(9);
const double up = x.dot(this->A_ * x);
const double down = x.dot(x);
const double mu = up / down;
// store the momentum with largest Rayleigh quotient and its according index of beta_
if (mu * mu / 4 > maxBeta) {
// save the max beta index
maxIndex = k;
maxBeta = mu * mu / 4;
}
}
}
// set beta_ to momentum with largest Rayleigh quotient
return betas[maxIndex];
}
/**
* Start the accelerated iteration, after performing the
* accelerated iteration, calculate the ritz error, repeat this
* operation until the ritz error converge. If converged return true, else
* false.
*/
bool compute(size_t maxIterations, double tol) {
// Starting
bool isConverged = false;
for (size_t i = 0; i < maxIterations && !isConverged; i++) {
++(this->nrIterations_);
Vector tmp = this->ritzVector_;
// update the ritzVector after accelerated power iteration
this->ritzVector_ = acceleratedPowerIteration();
// update the previousVector with ritzVector
previousVector_ = tmp;
// update the ritzValue
this->ritzValue_ = this->ritzVector_.dot(this->A_ * this->ritzVector_);
isConverged = this->converged(tol);
}
return isConverged;
}
};
} // namespace gtsam

80
gtsam/linear/GaussianFactorGraph.cpp
View File

@ -100,33 +100,35 @@ namespace gtsam {
}
/* ************************************************************************* */
vector<boost::tuple<size_t, size_t, double> > GaussianFactorGraph::sparseJacobian() const {
using SparseTriplets = std::vector<std::tuple<int, int, double> >;
SparseTriplets GaussianFactorGraph::sparseJacobian(const Ordering& ordering,
size_t& nrows,
size_t& ncols) const {
gttic_(GaussianFactorGraph_sparseJacobian);
// First find dimensions of each variable
typedef std::map<Key, size_t> KeySizeMap;
KeySizeMap dims;
for (const sharedFactor& factor : *this) {
if (!static_cast<bool>(factor))
continue;
for (const auto& factor : *this) {
if (!static_cast<bool>(factor)) continue;
for (GaussianFactor::const_iterator key = factor->begin();
key != factor->end(); ++key) {
dims[*key] = factor->getDim(key);
for (auto it = factor->begin(); it != factor->end(); ++it) {
dims[*it] = factor->getDim(it);
}
}
// Compute first scalar column of each variable
size_t currentColIndex = 0;
ncols = 0;
KeySizeMap columnIndices = dims;
for (const KeySizeMap::value_type& col : dims) {
columnIndices[col.first] = currentColIndex;
currentColIndex += dims[col.first];
for (const auto key : ordering) {
columnIndices[key] = ncols;
ncols += dims[key];
}
// Iterate over all factors, adding sparse scalar entries
typedef boost::tuple<size_t, size_t, double> triplet;
vector<triplet> entries;
size_t row = 0;
for (const sharedFactor& factor : *this) {
SparseTriplets entries;
entries.reserve(30 * size());
nrows = 0;
for (const auto& factor : *this) {
if (!static_cast<bool>(factor)) continue;
// Convert to JacobianFactor if necessary
@ -138,52 +140,60 @@ namespace gtsam {
if (hessian)
jacobianFactor.reset(new JacobianFactor(*hessian));
else
throw invalid_argument(
"GaussianFactorGraph contains a factor that is neither a JacobianFactor nor a HessianFactor.");
throw std::invalid_argument(
"GaussianFactorGraph contains a factor that is neither a "
"JacobianFactor nor a HessianFactor.");
}
// Whiten the factor and add entries for it
// iterate over all variables in the factor
const JacobianFactor whitened(jacobianFactor->whiten());
for (JacobianFactor::const_iterator key = whitened.begin();
key < whitened.end(); ++key) {
for (auto key = whitened.begin(); key < whitened.end(); ++key) {
JacobianFactor::constABlock whitenedA = whitened.getA(key);
// find first column index for this key
size_t column_start = columnIndices[*key];
for (size_t i = 0; i < (size_t) whitenedA.rows(); i++)
for (size_t j = 0; j < (size_t) whitenedA.cols(); j++) {
for (size_t i = 0; i < (size_t)whitenedA.rows(); i++)
for (size_t j = 0; j < (size_t)whitenedA.cols(); j++) {
double s = whitenedA(i, j);
if (std::abs(s) > 1e-12)
entries.push_back(boost::make_tuple(row + i, column_start + j, s));
entries.emplace_back(nrows + i, column_start + j, s);
}
}
JacobianFactor::constBVector whitenedb(whitened.getb());
size_t bcolumn = currentColIndex;
for (size_t i = 0; i < (size_t) whitenedb.size(); i++)
entries.push_back(boost::make_tuple(row + i, bcolumn, whitenedb(i)));
for (size_t i = 0; i < (size_t)whitenedb.size(); i++) {
double s = whitenedb(i);
if (std::abs(s) > 1e-12) entries.emplace_back(nrows + i, ncols, s);
}
// Increment row index
row += jacobianFactor->rows();
nrows += jacobianFactor->rows();
}
return vector<triplet>(entries.begin(), entries.end());
ncols++; // +1 for b-column
return entries;
}
/* ************************************************************************* */
SparseTriplets GaussianFactorGraph::sparseJacobian() const {
size_t nrows, ncols;
return sparseJacobian(Ordering(this->keys()), nrows, ncols);
}
/* ************************************************************************* */
Matrix GaussianFactorGraph::sparseJacobian_() const {
gttic_(GaussianFactorGraph_sparseJacobian_matrix);
// call sparseJacobian
typedef boost::tuple<size_t, size_t, double> triplet;
vector<triplet> result = sparseJacobian();
auto result = sparseJacobian();
// translate to base 1 matrix
size_t nzmax = result.size();
Matrix IJS(3,nzmax);
Matrix IJS(3, nzmax);
for (size_t k = 0; k < result.size(); k++) {
const triplet& entry = result[k];
IJS(0,k) = double(entry.get<0>() + 1);
IJS(1,k) = double(entry.get<1>() + 1);
IJS(2,k) = entry.get<2>();
const auto& entry = result[k];
IJS(0, k) = double(std::get<0>(entry) + 1);
IJS(1, k) = double(std::get<1>(entry) + 1);
IJS(2, k) = std::get<2>(entry);
}
return IJS;
}

20
gtsam/linear/GaussianFactorGraph.h
View File

@ -181,15 +181,25 @@ namespace gtsam {
///@{
/**
* Return vector of i, j, and s to generate an m-by-n sparse Jacobian matrix,
* where i(k) and j(k) are the base 0 row and column indices, s(k) a double.
* Returns a sparse augmented Jacbian matrix as a vector of i, j, and s,
* where i(k) and j(k) are the base 0 row and column indices, and s(k) is
* the entry as a double.
* The standard deviations are baked into A and b
* @return the sparse matrix as a std::vector of std::tuples
* @param ordering the column ordering
* @param[out] nrows The number of rows in the augmented Jacobian
* @param[out] ncols The number of columns in the augmented Jacobian
*/
std::vector<boost::tuple<size_t, size_t, double> > sparseJacobian() const;
std::vector<std::tuple<int, int, double> > sparseJacobian(
const Ordering& ordering, size_t& nrows, size_t& ncols) const;
/** Returns a sparse augmented Jacobian matrix with default ordering */
std::vector<std::tuple<int, int, double> > sparseJacobian() const;
/**
* Matrix version of sparseJacobian: generates a 3*m matrix with [i,j,s] entries
* such that S(i(k),j(k)) = s(k), which can be given to MATLAB's sparse.
* Matrix version of sparseJacobian: generates a 3*m matrix with [i,j,s]
* entries such that S(i(k),j(k)) = s(k), which can be given to MATLAB's
* sparse. Note: i, j are 1-indexed.
* The standard deviations are baked into A and b
*/
Matrix sparseJacobian_() const;

152
gtsam/linear/PowerMethod.h Normal file
View File

@ -0,0 +1,152 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010-2019, 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 PowerMethod.h
* @date Sept 2020
* @author Jing Wu
* @brief Power method for fast eigenvalue and eigenvector
* computation
*/
#pragma once
#include <gtsam/base/Matrix.h>
#include <gtsam/base/Vector.h>
#include <Eigen/Core>
#include <Eigen/Sparse>
#include <random>
#include <vector>
namespace gtsam {
using Sparse = Eigen::SparseMatrix<double>;
/**
* \brief Compute maximum Eigenpair with power method
*
* References :
* 1) G. Golub and C. V. Loan, Matrix Computations, 3rd ed. Baltimore, Johns
* Hopkins University Press, 1996, pp.405-411
* 2) Rosen, D. and Carlone, L., 2017, September. Computational
* enhancements for certifiably correct SLAM. In Proceedings of the
* International Conference on Intelligent Robots and Systems.
* 3) Yulun Tian and Kasra Khosoussi and David M. Rosen and Jonathan P. How,
* 2020, Aug, Distributed Certifiably Correct Pose-Graph Optimization, Arxiv
* 4) C. de Sa, B. He, I. Mitliagkas, C. Ré, and P. Xu, “Accelerated
* stochastic power iteration,” in Proc. Mach. Learn. Res., no. 84, 2018, pp.
* 58–67
*
* It performs the following iteration: \f$ x_{k+1} = A * x_k \f$
* where A is the aim matrix we want to get eigenpair of, x is the
* Ritz vector
*
* Template argument Operator just needs multiplication operator
*
*/
template <class Operator>
class PowerMethod {
protected:
/**
* Const reference to an externally-held matrix whose minimum-eigenvalue we
* want to compute
*/
const Operator &A_;
const int dim_; // dimension of Matrix A
size_t nrIterations_; // number of iterations
double ritzValue_; // Ritz eigenvalue
Vector ritzVector_; // Ritz eigenvector
public:
/// @name Standard Constructors
/// @{
/// Construct from the aim matrix and intial ritz vector
explicit PowerMethod(const Operator &A,
const boost::optional<Vector> initial = boost::none)
: A_(A), dim_(A.rows()), nrIterations_(0) {
Vector x0;
x0 = initial ? initial.get() : Vector::Random(dim_);
x0.normalize();
// initialize Ritz eigen value
ritzValue_ = 0.0;
// initialize Ritz eigen vector
ritzVector_ = powerIteration(x0);
}
/**
* Run power iteration to get ritzVector with previous ritzVector x, and
* return A * x / || A * x ||
*/
Vector powerIteration(const Vector &x) const {
Vector y = A_ * x;
y.normalize();
return y;
}
/**
* Run power iteration to get ritzVector with previous ritzVector x, and
* return A * x / || A * x ||
*/
Vector powerIteration() const { return powerIteration(ritzVector_); }
/**
* After Perform power iteration on a single Ritz value, check if the Ritz
* residual for the current Ritz pair is less than the required convergence
* tol, return true if yes, else false
*/
bool converged(double tol) const {
const Vector x = ritzVector_;
// store the Ritz eigen value
const double ritzValue = x.dot(A_ * x);
const double error = (A_ * x - ritzValue * x).norm();
return error < tol;
}
/// Return the number of iterations
size_t nrIterations() const { return nrIterations_; }
/**
* Start the power/accelerated iteration, after performing the
* power/accelerated iteration, calculate the ritz error, repeat this
* operation until the ritz error converge. If converged return true, else
* false.
*/
bool compute(size_t maxIterations, double tol) {
// Starting
bool isConverged = false;
for (size_t i = 0; i < maxIterations && !isConverged; i++) {
++nrIterations_;
// update the ritzVector after power iteration
ritzVector_ = powerIteration();
// update the ritzValue
ritzValue_ = ritzVector_.dot(A_ * ritzVector_);
isConverged = converged(tol);
}
return isConverged;
}
/// Return the eigenvalue
double eigenvalue() const { return ritzValue_; }
/// Return the eigenvector
Vector eigenvector() const { return ritzVector_; }
};
} // namespace gtsam

63
gtsam/linear/SparseEigen.h Normal file
View File

@ -0,0 +1,63 @@
/* ----------------------------------------------------------------------------
* 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 SparseEigen.h
*
* @brief Utilities for creating Eigen sparse matrices (gtsam::SparseEigen)
*
* @date Aug 2019
* @author Mandy Xie
* @author Fan Jiang
* @author Gerry Chen
* @author Frank Dellaert
*/
#pragma once
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/VectorValues.h>
#include <Eigen/Sparse>
namespace gtsam {
/// Eigen-format sparse matrix. Note: ColMajor is ~20% faster since
/// InnerIndices must be sorted
typedef Eigen::SparseMatrix<double, Eigen::ColMajor, int> SparseEigen;
/// Constructs an Eigen-format SparseMatrix of a GaussianFactorGraph
SparseEigen sparseJacobianEigen(
const GaussianFactorGraph &gfg, const Ordering &ordering) {
gttic_(SparseEigen_sparseJacobianEigen);
// intermediate `entries` vector is kind of unavoidable due to how expensive
// factor->rows() is, which prevents us from populating SparseEigen directly.
size_t nrows, ncols;
auto entries = gfg.sparseJacobian(ordering, nrows, ncols);
// declare sparse matrix
SparseEigen Ab(nrows, ncols);
// See Eigen::set_from_triplets. This is about 5% faster.
// pass 1: count the nnz per inner-vector
std::vector<int> nnz(ncols, 0);
for (const auto &entry : entries) nnz[std::get<1>(entry)]++;
Ab.reserve(nnz);
// pass 2: insert the elements
for (const auto &entry : entries)
Ab.insert(std::get<0>(entry), std::get<1>(entry)) = std::get<2>(entry);
return Ab;
}
SparseEigen sparseJacobianEigen(const GaussianFactorGraph &gfg) {
gttic_(SparseEigen_sparseJacobianEigen_defaultOrdering);
return sparseJacobianEigen(gfg, Ordering(gfg.keys()));
}
} // namespace gtsam

67
gtsam/linear/tests/powerMethodExample.h Normal file
View File

@ -0,0 +1,67 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010-2019, 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
* -------------------------------------------------------------------------- */
/**
* powerMethodExample.h
*
* @file powerMethodExample.h
* @date Nov 2020
* @author Jing Wu
* @brief Create sparse and dense factor graph for
* PowerMethod/AcceleratedPowerMethod
*/
#include <gtsam/inference/Symbol.h>
#include <iostream>
namespace gtsam {
namespace linear {
namespace test {
namespace example {
/* ************************************************************************* */
inline GaussianFactorGraph createSparseGraph() {
using symbol_shorthand::X;
// Let's make a scalar synchronization graph with 4 nodes
GaussianFactorGraph fg;
auto model = noiseModel::Unit::Create(1);
for (size_t j = 0; j < 3; j++) {
fg.add(X(j), -I_1x1, X(j + 1), I_1x1, Vector1::Zero(), model);
}
fg.add(X(3), -I_1x1, X(0), I_1x1, Vector1::Zero(), model); // extra row
return fg;
}
/* ************************************************************************* */
inline GaussianFactorGraph createDenseGraph() {
using symbol_shorthand::X;
// Let's make a scalar synchronization graph with 10 nodes
GaussianFactorGraph fg;
auto model = noiseModel::Unit::Create(1);
// Iterate over nodes
for (size_t j = 0; j < 10; j++) {
// Each node has an edge with all the others
for (size_t i = 1; i < 10; i++)
fg.add(X(j), -I_1x1, X((j + i) % 10), I_1x1, Vector1::Zero(), model);
}
return fg;
}
/* ************************************************************************* */
} // namespace example
} // namespace test
} // namespace linear
} // namespace gtsam

140
gtsam/linear/tests/testAcceleratedPowerMethod.cpp Normal file
View File

@ -0,0 +1,140 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010-2019, 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
* -------------------------------------------------------------------------- */
/**
* testPowerMethod.cpp
*
* @file testAcceleratedPowerMethod.cpp
* @date Sept 2020
* @author Jing Wu
* @brief Check eigenvalue and eigenvector computed by accelerated power method
*/
#include <CppUnitLite/TestHarness.h>
#include <gtsam/base/Matrix.h>
#include <gtsam/base/VectorSpace.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/linear/AcceleratedPowerMethod.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/tests/powerMethodExample.h>
#include <Eigen/Core>
#include <Eigen/Dense>
#include <Eigen/Eigenvalues>
#include <iostream>
#include <random>
using namespace std;
using namespace gtsam;
/* ************************************************************************* */
TEST(AcceleratedPowerMethod, acceleratedPowerIteration) {
// test power iteration, beta is set to 0
Sparse A(6, 6);
A.coeffRef(0, 0) = 6;
A.coeffRef(1, 1) = 5;
A.coeffRef(2, 2) = 4;
A.coeffRef(3, 3) = 3;
A.coeffRef(4, 4) = 2;
A.coeffRef(5, 5) = 1;
Vector initial = (Vector(6) << 0.24434602, 0.22829942, 0.70094486, 0.15463092, 0.55871359,
0.2465342).finished();
const double ev1 = 6.0;
// test accelerated power iteration
AcceleratedPowerMethod<Sparse> apf(A, initial);
apf.compute(100, 1e-5);
EXPECT_LONGS_EQUAL(6, apf.eigenvector().rows());
Vector6 actual1 = apf.eigenvector();
const double ritzValue = actual1.dot(A * actual1);
const double ritzResidual = (A * actual1 - ritzValue * actual1).norm();
EXPECT_DOUBLES_EQUAL(0, ritzResidual, 1e-5);
EXPECT_DOUBLES_EQUAL(ev1, apf.eigenvalue(), 1e-5);
}
/* ************************************************************************* */
TEST(AcceleratedPowerMethod, useFactorGraphSparse) {
// Let's make a scalar synchronization graph with 4 nodes
GaussianFactorGraph fg = gtsam::linear::test::example::createSparseGraph();
// Get eigenvalues and eigenvectors with Eigen
auto L = fg.hessian();
Eigen::EigenSolver<Matrix> solver(L.first);
// find the index of the max eigenvalue
size_t maxIdx = 0;
for (auto i = 0; i < solver.eigenvalues().rows(); ++i) {
if (solver.eigenvalues()(i).real() >= solver.eigenvalues()(maxIdx).real())
maxIdx = i;
}
// Store the max eigenvalue and its according eigenvector
const auto ev1 = solver.eigenvalues()(maxIdx).real();
Vector disturb = Vector4::Random();
disturb.normalize();
Vector initial = L.first.row(0);
double magnitude = initial.norm();
initial += 0.03 * magnitude * disturb;
AcceleratedPowerMethod<Matrix> apf(L.first, initial);
apf.compute(100, 1e-5);
// Check if the eigenvalue is the maximum eigen value
EXPECT_DOUBLES_EQUAL(ev1, apf.eigenvalue(), 1e-8);
// Check if the according ritz residual converged to the threshold
Vector actual1 = apf.eigenvector();
const double ritzValue = actual1.dot(L.first * actual1);
const double ritzResidual = (L.first * actual1 - ritzValue * actual1).norm();
EXPECT_DOUBLES_EQUAL(0, ritzResidual, 1e-5);
}
/* ************************************************************************* */
TEST(AcceleratedPowerMethod, useFactorGraphDense) {
// Let's make a scalar synchronization graph with 10 nodes
GaussianFactorGraph fg = gtsam::linear::test::example::createDenseGraph();
// Get eigenvalues and eigenvectors with Eigen
auto L = fg.hessian();
Eigen::EigenSolver<Matrix> solver(L.first);
// find the index of the max eigenvalue
size_t maxIdx = 0;
for (auto i = 0; i < solver.eigenvalues().rows(); ++i) {
if (solver.eigenvalues()(i).real() >= solver.eigenvalues()(maxIdx).real())
maxIdx = i;
}
// Store the max eigenvalue and its according eigenvector
const auto ev1 = solver.eigenvalues()(maxIdx).real();
Vector disturb = Vector10::Random();
disturb.normalize();
Vector initial = L.first.row(0);
double magnitude = initial.norm();
initial += 0.03 * magnitude * disturb;
AcceleratedPowerMethod<Matrix> apf(L.first, initial);
apf.compute(100, 1e-5);
// Check if the eigenvalue is the maximum eigen value
EXPECT_DOUBLES_EQUAL(ev1, apf.eigenvalue(), 1e-8);
// Check if the according ritz residual converged to the threshold
Vector actual1 = apf.eigenvector();
const double ritzValue = actual1.dot(L.first * actual1);
const double ritzResidual = (L.first * actual1 - ritzValue * actual1).norm();
EXPECT_DOUBLES_EQUAL(0, ritzResidual, 1e-5);
}
/* ************************************************************************* */
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */

53
gtsam/linear/tests/testGaussianFactorGraph.cpp
View File

@ -36,9 +36,18 @@ using namespace boost::assign;
using namespace std;
using namespace gtsam;
// static SharedDiagonal
// sigma0_1 = noiseModel::Isotropic::Sigma(2,0.1), sigma_02 = noiseModel::Isotropic::Sigma(2,0.2),
// constraintModel = noiseModel::Constrained::All(2);
typedef std::tuple<size_t, size_t, double> SparseTriplet;
bool triplet_equal(SparseTriplet a, SparseTriplet b) {
if (get<0>(a) == get<0>(b) && get<1>(a) == get<1>(b) &&
get<2>(a) == get<2>(b)) return true;
cout << "not equal:" << endl;
cout << "\texpected: "
"(" << get<0>(a) << ", " << get<1>(a) << ") = " << get<2>(a) << endl;
cout << "\tactual: "
"(" << get<0>(b) << ", " << get<1>(b) << ") = " << get<2>(b) << endl;
return false;
}
/* ************************************************************************* */
TEST(GaussianFactorGraph, initialization) {
@ -57,10 +66,11 @@ TEST(GaussianFactorGraph, initialization) {
// Test sparse, which takes a vector and returns a matrix, used in MATLAB
// Note that this the augmented vector and the RHS is in column 7
Matrix expectedIJS =
(Matrix(3, 22) << 1., 2., 1., 2., 3., 4., 3., 4., 3., 4., 5., 6., 5., 6., 5., 6., 7., 8., 7.,
8., 7., 8., 1., 2., 7., 7., 1., 2., 3., 4., 7., 7., 1., 2., 5., 6., 7., 7., 3., 4., 5., 6.,
7., 7., 10., 10., -1., -1., -10., -10., 10., 10., 2., -1., -5., -5., 5., 5., 0., 1., -5.,
-5., 5., 5., -1., 1.5).finished();
(Matrix(3, 21) <<
1., 2., 1., 2., 3., 4., 3., 4., 3., 4., 5., 6., 5., 6., 6., 7., 8., 7., 8., 7., 8.,
1., 2., 7., 7., 1., 2., 3., 4., 7., 7., 1., 2., 5., 6., 7., 3., 4., 5., 6., 7., 7.,
10., 10., -1., -1., -10., -10., 10., 10., 2., -1., -5., -5., 5., 5.,
1., -5., -5., 5., 5., -1., 1.5).finished();
Matrix actualIJS = fg.sparseJacobian_();
EQUALITY(expectedIJS, actualIJS);
}
@ -74,8 +84,8 @@ TEST(GaussianFactorGraph, sparseJacobian) {
// 9 10 0 11 12 13
// 0 0 0 14 15 16
// Expected - NOTE that we transpose this!
Matrix expectedT = (Matrix(16, 3) <<
// Expected
Matrix expected = (Matrix(16, 3) <<
1., 1., 2.,
1., 2., 4.,
1., 3., 6.,
@ -93,17 +103,32 @@ TEST(GaussianFactorGraph, sparseJacobian) {
3., 6.,26.,
4., 6.,32.).finished();
Matrix expected = expectedT.transpose();
// expected: in matlab format - NOTE the transpose!)
Matrix expectedMatlab = expected.transpose();
GaussianFactorGraph gfg;
SharedDiagonal model = noiseModel::Isotropic::Sigma(2, 0.5);
gfg.add(0, (Matrix(2, 3) << 1., 2., 3., 5., 6., 7.).finished(), Vector2(4., 8.), model);
gfg.add(0, (Matrix(2, 3) << 9., 10., 0., 0., 0., 0.).finished(), 1,
(Matrix(2, 2) << 11., 12., 14., 15.).finished(), Vector2(13., 16.), model);
const Key x123 = 0, x45 = 1;
gfg.add(x123, (Matrix(2, 3) << 1, 2, 3, 5, 6, 7).finished(),
Vector2(4, 8), model);
gfg.add(x123, (Matrix(2, 3) << 9, 10, 0, 0, 0, 0).finished(),
x45, (Matrix(2, 2) << 11, 12, 14, 15.).finished(),
Vector2(13, 16), model);
Matrix actual = gfg.sparseJacobian_();
EXPECT(assert_equal(expected, actual));
EXPECT(assert_equal(expectedMatlab, actual));
// SparseTriplets
auto boostActual = gfg.sparseJacobian();
// check the triplets size...
EXPECT_LONGS_EQUAL(16, boostActual.size());
// check content
for (int i = 0; i < 16; i++) {
EXPECT(triplet_equal(
SparseTriplet(expected(i, 0) - 1, expected(i, 1) - 1, expected(i, 2)),
boostActual.at(i)));
}
}
/* ************************************************************************* */

124
gtsam/linear/tests/testPowerMethod.cpp Normal file
View File

@ -0,0 +1,124 @@
/* ----------------------------------------------------------------------------
* GTSAM Copyright 2010-2019, 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
* -------------------------------------------------------------------------- */
/**
* testPowerMethod.cpp
*
* @file testPowerMethod.cpp
* @date Sept 2020
* @author Jing Wu
* @brief Check eigenvalue and eigenvector computed by power method
*/
#include <CppUnitLite/TestHarness.h>
#include <gtsam/base/Matrix.h>
#include <gtsam/base/VectorSpace.h>
#include <gtsam/inference/Symbol.h>
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/PowerMethod.h>
#include <gtsam/linear/tests/powerMethodExample.h>
#include <Eigen/Core>
#include <Eigen/Dense>
#include <Eigen/Eigenvalues>
#include <iostream>
#include <random>
using namespace std;
using namespace gtsam;
/* ************************************************************************* */
TEST(PowerMethod, powerIteration) {
// test power iteration, beta is set to 0
Sparse A(6, 6);
A.coeffRef(0, 0) = 6;
A.coeffRef(1, 1) = 5;
A.coeffRef(2, 2) = 4;
A.coeffRef(3, 3) = 3;
A.coeffRef(4, 4) = 2;
A.coeffRef(5, 5) = 1;
Vector initial = (Vector(6) << 0.24434602, 0.22829942, 0.70094486, 0.15463092, 0.55871359,
0.2465342).finished();
PowerMethod<Sparse> pf(A, initial);
pf.compute(100, 1e-5);
EXPECT_LONGS_EQUAL(6, pf.eigenvector().rows());
Vector6 actual1 = pf.eigenvector();
const double ritzValue = actual1.dot(A * actual1);
const double ritzResidual = (A * actual1 - ritzValue * actual1).norm();
EXPECT_DOUBLES_EQUAL(0, ritzResidual, 1e-5);
const double ev1 = 6.0;
EXPECT_DOUBLES_EQUAL(ev1, pf.eigenvalue(), 1e-5);
}
/* ************************************************************************* */
TEST(PowerMethod, useFactorGraphSparse) {
// Let's make a scalar synchronization graph with 4 nodes
GaussianFactorGraph fg = gtsam::linear::test::example::createSparseGraph();
// Get eigenvalues and eigenvectors with Eigen
auto L = fg.hessian();
Eigen::EigenSolver<Matrix> solver(L.first);
// find the index of the max eigenvalue
size_t maxIdx = 0;
for (auto i = 0; i < solver.eigenvalues().rows(); ++i) {
if (solver.eigenvalues()(i).real() >= solver.eigenvalues()(maxIdx).real())
maxIdx = i;
}
// Store the max eigenvalue and its according eigenvector
const auto ev1 = solver.eigenvalues()(maxIdx).real();
Vector initial = Vector4::Random();
PowerMethod<Matrix> pf(L.first, initial);
pf.compute(100, 1e-5);
EXPECT_DOUBLES_EQUAL(ev1, pf.eigenvalue(), 1e-8);
auto actual2 = pf.eigenvector();
const double ritzValue = actual2.dot(L.first * actual2);
const double ritzResidual = (L.first * actual2 - ritzValue * actual2).norm();
EXPECT_DOUBLES_EQUAL(0, ritzResidual, 1e-5);
}
/* ************************************************************************* */
TEST(PowerMethod, useFactorGraphDense) {
// Let's make a scalar synchronization graph with 10 nodes
GaussianFactorGraph fg = gtsam::linear::test::example::createDenseGraph();
// Get eigenvalues and eigenvectors with Eigen
auto L = fg.hessian();
Eigen::EigenSolver<Matrix> solver(L.first);
// find the index of the max eigenvalue
size_t maxIdx = 0;
for (auto i = 0; i < solver.eigenvalues().rows(); ++i) {
if (solver.eigenvalues()(i).real() >= solver.eigenvalues()(maxIdx).real())
maxIdx = i;
}
// Store the max eigenvalue and its according eigenvector
const auto ev1 = solver.eigenvalues()(maxIdx).real();
Vector initial = Vector10::Random();
PowerMethod<Matrix> pf(L.first, initial);
pf.compute(100, 1e-5);
EXPECT_DOUBLES_EQUAL(ev1, pf.eigenvalue(), 1e-8);
auto actual2 = pf.eigenvector();
const double ritzValue = actual2.dot(L.first * actual2);
const double ritzResidual = (L.first * actual2 - ritzValue * actual2).norm();
EXPECT_DOUBLES_EQUAL(0, ritzResidual, 1e-5);
}
/* ************************************************************************* */
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */

72
gtsam/linear/tests/testSparseEigen.cpp Normal file
View File

@ -0,0 +1,72 @@
/* ----------------------------------------------------------------------------
* 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 testSparseMatrix.cpp
* @author Mandy Xie
* @author Fan Jiang
* @author Gerry Chen
* @author Frank Dellaert
* @date Jan, 2021
*/
#include <gtsam/linear/GaussianFactorGraph.h>
#include <gtsam/linear/SparseEigen.h>
#include <boost/assign/list_of.hpp>
using boost::assign::list_of;
#include <gtsam/base/TestableAssertions.h>
#include <CppUnitLite/TestHarness.h>
using namespace std;
using namespace gtsam;
/* ************************************************************************* */
TEST(SparseEigen, sparseJacobianEigen) {
GaussianFactorGraph gfg;
SharedDiagonal model = noiseModel::Isotropic::Sigma(2, 0.5);
const Key x123 = 0, x45 = 1;
gfg.add(x123, (Matrix(2, 3) << 1, 2, 3, 5, 6, 7).finished(),
Vector2(4, 8), model);
gfg.add(x123, (Matrix(2, 3) << 9, 10, 0, 0, 0, 0).finished(),
x45, (Matrix(2, 2) << 11, 12, 14, 15.).finished(),
Vector2(13, 16), model);
// Sparse Matrix
auto sparseResult = sparseJacobianEigen(gfg);
EXPECT_LONGS_EQUAL(16, sparseResult.nonZeros());
EXPECT(assert_equal(4, sparseResult.rows()));
EXPECT(assert_equal(6, sparseResult.cols()));
EXPECT(assert_equal(gfg.augmentedJacobian(), Matrix(sparseResult)));
// Call sparseJacobian with optional ordering...
auto ordering = Ordering(list_of(x45)(x123));
// Eigen Sparse with optional ordering
EXPECT(assert_equal(gfg.augmentedJacobian(ordering),
Matrix(sparseJacobianEigen(gfg, ordering))));
// Check matrix dimensions when zero rows / cols
gfg.add(x123, Matrix23::Zero(), Vector2::Zero(), model); // zero row
gfg.add(2, Matrix21::Zero(), Vector2::Zero(), model); // zero col
sparseResult = sparseJacobianEigen(gfg);
EXPECT_LONGS_EQUAL(16, sparseResult.nonZeros());
EXPECT(assert_equal(8, sparseResult.rows()));
EXPECT(assert_equal(7, sparseResult.cols()));
}
/* ************************************************************************* */
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */

2
gtsam/navigation/CombinedImuFactor.cpp
View File

@ -167,7 +167,7 @@ gtsam::NonlinearFactor::shared_ptr CombinedImuFactor::clone() const {
//------------------------------------------------------------------------------
void CombinedImuFactor::print(const string& s,
const KeyFormatter& keyFormatter) const {
cout << (s == "" ? s : s + "\n") << "CombinedImuFactor("
cout << (s.empty() ? s : s + "\n") << "CombinedImuFactor("
<< keyFormatter(this->key1()) << "," << keyFormatter(this->key2()) << ","
<< keyFormatter(this->key3()) << "," << keyFormatter(this->key4()) << ","
<< keyFormatter(this->key5()) << "," << keyFormatter(this->key6())

4
gtsam/navigation/ImuFactor.cpp
View File

@ -130,7 +130,7 @@ std::ostream& operator<<(std::ostream& os, const ImuFactor& f) {
//------------------------------------------------------------------------------
void ImuFactor::print(const string& s, const KeyFormatter& keyFormatter) const {
cout << (s == "" ? s : s + "\n") << "ImuFactor(" << keyFormatter(this->key1())
cout << (s.empty() ? s : s + "\n") << "ImuFactor(" << keyFormatter(this->key1())
<< "," << keyFormatter(this->key2()) << "," << keyFormatter(this->key3())
<< "," << keyFormatter(this->key4()) << "," << keyFormatter(this->key5())
<< ")\n";
@ -226,7 +226,7 @@ std::ostream& operator<<(std::ostream& os, const ImuFactor2& f) {
//------------------------------------------------------------------------------
void ImuFactor2::print(const string& s,
const KeyFormatter& keyFormatter) const {
cout << (s == "" ? s : s + "\n") << "ImuFactor2("
cout << (s.empty() ? s : s + "\n") << "ImuFactor2("
<< keyFormatter(this->key1()) << "," << keyFormatter(this->key2()) << ","
<< keyFormatter(this->key3()) << ")\n";
cout << *this << endl;

2
gtsam/navigation/PreintegratedRotation.cpp
View File

@ -26,7 +26,7 @@ using namespace std;
namespace gtsam {
void PreintegratedRotationParams::print(const string& s) const {
cout << (s == "" ? s : s + "\n") << endl;
cout << (s.empty() ? s : s + "\n") << endl;
cout << "gyroscopeCovariance:\n[\n" << gyroscopeCovariance << "\n]" << endl;
if (omegaCoriolis)
cout << "omegaCoriolis = (" << omegaCoriolis->transpose() << ")" << endl;

2
gtsam/navigation/PreintegrationBase.cpp
View File

@ -46,7 +46,7 @@ ostream& operator<<(ostream& os, const PreintegrationBase& pim) {
//------------------------------------------------------------------------------
void PreintegrationBase::print(const string& s) const {
cout << (s == "" ? s : s + "\n") << *this << endl;
cout << (s.empty() ? s : s + "\n") << *this << endl;
}
//------------------------------------------------------------------------------

2
gtsam/nonlinear/Values.cpp
View File

@ -75,7 +75,7 @@ namespace gtsam {
/* ************************************************************************* */
void Values::print(const string& str, const KeyFormatter& keyFormatter) const {
cout << str << (str == "" ? "" : "\n");
cout << str << (str.empty() ? "" : "\n");
cout << "Values with " << size() << " values:\n";
for(const_iterator key_value = begin(); key_value != end(); ++key_value) {
cout << "Value " << keyFormatter(key_value->key) << ": ";

105
gtsam/sfm/ShonanAveraging.cpp
View File

@ -17,6 +17,7 @@
*/
#include <SymEigsSolver.h>
#include <cmath>
#include <gtsam/linear/PCGSolver.h>
#include <gtsam/linear/SubgraphPreconditioner.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
@ -487,8 +488,88 @@ Sparse ShonanAveraging<d>::computeA(const Values &values) const {
return Lambda - Q_;
}
/* ************************************************************************* */
template <size_t d>
Sparse ShonanAveraging<d>::computeA(const Matrix &S) const {
auto Lambda = computeLambda(S);
return Lambda - Q_;
}
/* ************************************************************************* */
// Perturb the initial initialVector by adding a spherically-uniformly
// distributed random vector with 0.03*||initialVector||_2 magnitude to
// initialVector
// ref : Part III. C, Rosen, D. and Carlone, L., 2017, September. Computational
// enhancements for certifiably correct SLAM. In Proceedings of the
// International Conference on Intelligent Robots and Systems.
static Vector perturb(const Vector &initialVector) {
// generate a 0.03*||x_0||_2 as stated in David's paper
int n = initialVector.rows();
Vector disturb = Vector::Random(n);
disturb.normalize();
double magnitude = initialVector.norm();
Vector perturbedVector = initialVector + 0.03 * magnitude * disturb;
perturbedVector.normalize();
return perturbedVector;
}
/* ************************************************************************* */
/// MINIMUM EIGENVALUE COMPUTATIONS
// Alg.6 from paper Distributed Certifiably Correct Pose-Graph Optimization,
// it takes in the certificate matrix A as input, the maxIterations and the
// minEigenvalueNonnegativityTolerance is set to 1000 and 10e-4 ad default,
// there are two parts
// in this algorithm:
// (1) compute the maximum eigenpair (\lamda_dom, \vect{v}_dom) of A by power
// method. if \lamda_dom is less than zero, then return the eigenpair. (2)
// compute the maximum eigenpair (\theta, \vect{v}) of C = \lamda_dom * I - A by
// accelerated power method. Then return (\lamda_dom - \theta, \vect{v}).
static bool PowerMinimumEigenValue(
const Sparse &A, const Matrix &S, double &minEigenValue,
Vector *minEigenVector = 0, size_t *numIterations = 0,
size_t maxIterations = 1000,
double minEigenvalueNonnegativityTolerance = 10e-4) {
// a. Compute dominant eigenpair of S using power method
PowerMethod<Sparse> pmOperator(A);
const bool pmConverged = pmOperator.compute(
maxIterations, 1e-5);
// Check convergence and bail out if necessary
if (!pmConverged) return false;
const double pmEigenValue = pmOperator.eigenvalue();
if (pmEigenValue < 0) {
// The largest-magnitude eigenvalue is negative, and therefore also the
// minimum eigenvalue, so just return this solution
minEigenValue = pmEigenValue;
if (minEigenVector) {
*minEigenVector = pmOperator.eigenvector();
minEigenVector->normalize(); // Ensure that this is a unit vector
}
return true;
}
const Sparse C = pmEigenValue * Matrix::Identity(A.rows(), A.cols()) - A;
const boost::optional<Vector> initial = perturb(S.row(0));
AcceleratedPowerMethod<Sparse> apmShiftedOperator(C, initial);
const bool minConverged = apmShiftedOperator.compute(
maxIterations, minEigenvalueNonnegativityTolerance / pmEigenValue);
if (!minConverged) return false;
minEigenValue = pmEigenValue - apmShiftedOperator.eigenvalue();
if (minEigenVector) {
*minEigenVector = apmShiftedOperator.eigenvector();
minEigenVector->normalize(); // Ensure that this is a unit vector
}
if (numIterations) *numIterations = apmShiftedOperator.nrIterations();
return true;
}
/** This is a lightweight struct used in conjunction with Spectra to compute
* the minimum eigenvalue and eigenvector of a sparse matrix A; it has a single
@ -634,13 +715,6 @@ static bool SparseMinimumEigenValue(
return true;
}
/* ************************************************************************* */
template <size_t d>
Sparse ShonanAveraging<d>::computeA(const Matrix &S) const {
auto Lambda = computeLambda(S);
return Lambda - Q_;
}
/* ************************************************************************* */
template <size_t d>
double ShonanAveraging<d>::computeMinEigenValue(const Values &values,
@ -658,6 +732,23 @@ double ShonanAveraging<d>::computeMinEigenValue(const Values &values,
return minEigenValue;
}
/* ************************************************************************* */
template <size_t d>
double ShonanAveraging<d>::computeMinEigenValueAP(const Values &values,
Vector *minEigenVector) const {
assert(values.size() == nrUnknowns());
const Matrix S = StiefelElementMatrix(values);
auto A = computeA(S);
double minEigenValue = 0;
bool success = PowerMinimumEigenValue(A, S, minEigenValue, minEigenVector);
if (!success) {
throw std::runtime_error(
"PowerMinimumEigenValue failed to compute minimum eigenvalue.");
}
return minEigenValue;
}
/* ************************************************************************* */
template <size_t d>
std::pair<double, Vector> ShonanAveraging<d>::computeMinEigenVector(

12
gtsam/sfm/ShonanAveraging.h
View File

@ -26,6 +26,8 @@
#include <gtsam/linear/VectorValues.h>
#include <gtsam/nonlinear/LevenbergMarquardtParams.h>
#include <gtsam/sfm/BinaryMeasurement.h>
#include <gtsam/linear/PowerMethod.h>
#include <gtsam/linear/AcceleratedPowerMethod.h>
#include <gtsam/slam/dataset.h>
#include <Eigen/Sparse>
@ -89,7 +91,8 @@ struct GTSAM_EXPORT ShonanAveragingParameters {
bool getCertifyOptimality() const { return certifyOptimality; }
/// Print the parameters and flags used for rotation averaging.
void print() const {
void print(const std::string &s = "") const {
std::cout << (s.empty() ? s : s + " ");
std::cout << " ShonanAveragingParameters: " << std::endl;
std::cout << " alpha: " << alpha << std::endl;
std::cout << " beta: " << beta << std::endl;
@ -250,6 +253,13 @@ class GTSAM_EXPORT ShonanAveraging {
double computeMinEigenValue(const Values &values,
Vector *minEigenVector = nullptr) const;
/**
* Compute minimum eigenvalue with accelerated power method.
* @param values: should be of type SOn
*/
double computeMinEigenValueAP(const Values &values,
Vector *minEigenVector = nullptr) const;
/// Project pxdN Stiefel manifold matrix S to Rot3^N
Values roundSolutionS(const Matrix &S) const;

5
gtsam/sfm/tests/testShonanAveraging.cpp
View File

@ -188,9 +188,14 @@ TEST(ShonanAveraging3, CheckWithEigen) {
for (int i = 1; i < lambdas.size(); i++)
minEigenValue = min(lambdas(i), minEigenValue);
// Compute Eigenvalue with Accelerated Power method
double lambdaAP = kShonan.computeMinEigenValueAP(Qstar3);
// Actual check
EXPECT_DOUBLES_EQUAL(0, lambda, 1e-11);
EXPECT_DOUBLES_EQUAL(0, minEigenValue, 1e-11);
EXPECT_DOUBLES_EQUAL(0, lambdaAP, 1e-11);
// Construct test descent direction (as minEigenVector is not predictable
// across platforms, being one from a basically flat 3d- subspace)

8
gtsam_unstable/slam/SmartStereoProjectionPoseFactor.h
View File

@ -113,9 +113,9 @@ class SmartStereoProjectionPoseFactor : public SmartStereoProjectionFactor {
* print
* @param s optional string naming the factor
* @param keyFormatter optional formatter useful for printing Symbols
*/ void print(
const std::string& s = "",
const KeyFormatter& keyFormatter = DefaultKeyFormatter) const override;
*/
void print(const std::string& s = "", const KeyFormatter& keyFormatter =
DefaultKeyFormatter) const override;
/// equals
bool equals(const NonlinearFactor& p, double tol = 1e-9) const override;
@ -155,4 +155,4 @@ template <>
struct traits<SmartStereoProjectionPoseFactor>
: public Testable<SmartStereoProjectionPoseFactor> {};
} // \ namespace gtsam
} // namespace gtsam

35
tests/ImuMeasurement.h Normal file
View File

@ -0,0 +1,35 @@
#pragma once
#include <tests/Measurement.h>
namespace gtsam {
/**
*\brief Contains data from the IMU mesaurements.
*/
class ImuMeasurement : public Measurement {
public:
enum Name { BODY = 0, RF_FOOT = 1, RH_FOOT = 2 };
Name name; ///< Unique string identifier
Eigen::Vector3d I_a_WI; ///< Raw acceleration from the IMU (m/s/s)
Eigen::Vector3d I_w_WI; ///< Raw angular velocity from the IMU (rad/s)
ImuMeasurement()
: Measurement("ImuMeasurement"), I_a_WI{0, 0, 0}, I_w_WI{0, 0, 0} {}
virtual ~ImuMeasurement() override {}
friend std::ostream& operator<<(std::ostream& stream,
const ImuMeasurement& meas);
};
std::ostream& operator<<(std::ostream& stream, const ImuMeasurement& meas) {
stream << "IMU Measurement at time = " << meas.time << " : \n"
<< "dt : " << meas.dt << "\n"
<< "I_a_WI: " << meas.I_a_WI.transpose() << "\n"
<< "I_w_WI: " << meas.I_w_WI.transpose() << "\n";
return stream;
}
} // namespace gtsam

25
tests/Measurement.h Normal file
View File

@ -0,0 +1,25 @@
#pragma once
#include <Eigen/Core>
#include <string>
namespace gtsam {
/**
* \brief This is the base class for all measurement types.
*/
class Measurement {
public:
EIGEN_MAKE_ALIGNED_OPERATOR_NEW
size_t dt; ///< Time since the last message of this type (nanoseconds).
size_t time; ///< ROS time message recieved (nanoseconds).
///< The type of message (to enable dynamic/static casting).
std::string type;
Measurement() : dt(0), time(0), type("UNDEFINED") {}
Measurement(std::string _type) : dt(0), time(0), type(_type) {}
virtual ~Measurement() {}
};
} // namespace gtsam

127
tests/testImuPreintegration.cpp Normal file
View File

@ -0,0 +1,127 @@
/* ----------------------------------------------------------------------------
* 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 testImuPreintegration.cpp
* @brief Unit tests for IMU Preintegration
* @author Russell Buchanan
**/
#include <CppUnitLite/TestHarness.h>
#include <gtsam/base/Testable.h>
#include <gtsam/base/numericalDerivative.h>
#include <gtsam/navigation/CombinedImuFactor.h>
#include <gtsam/slam/dataset.h>
#include <tests/ImuMeasurement.h>
#include <fstream>
#include <iostream>
using namespace std;
using namespace gtsam;
/* ************************************************************************* */
/**
* \brief Uses the GTSAM library to perform IMU preintegration on an
* acceleration input.
*/
TEST(TestImuPreintegration, LoadedSimulationData) {
Vector3 finalPos(0, 0, 0);
vector<ImuMeasurement> imuMeasurements;
double accNoiseSigma = 0.001249;
double accBiasRwSigma = 0.000106;
double gyrNoiseSigma = 0.000208;
double gyrBiasRwSigma = 0.000004;
double integrationCovariance = 1e-8;
double biasAccOmegaInt = 1e-5;
double gravity = 9.81;
double rate = 400.0; // Hz
string inFileString = findExampleDataFile("quadraped_imu_data.csv");
ifstream inputFile(inFileString);
string line;
while (getline(inputFile, line)) {
stringstream ss(line);
string str;
vector<double> results;
while (getline(ss, str, ',')) {
results.push_back(atof(str.c_str()));
}
ImuMeasurement measurement;
measurement.dt = static_cast<size_t>(1e9 * (1 / rate));
measurement.time = results[2];
measurement.I_a_WI = {results[29], results[30], results[31]};
measurement.I_w_WI = {results[17], results[18], results[19]};
imuMeasurements.push_back(measurement);
}
// Assume a Z-up navigation (assuming we are performing optimization in the
// IMU frame).
auto imuPreintegratedParams =
PreintegratedCombinedMeasurements::Params::MakeSharedU(gravity);
imuPreintegratedParams->accelerometerCovariance =
I_3x3 * pow(accNoiseSigma, 2);
imuPreintegratedParams->biasAccCovariance = I_3x3 * pow(accBiasRwSigma, 2);
imuPreintegratedParams->gyroscopeCovariance = I_3x3 * pow(gyrNoiseSigma, 2);
imuPreintegratedParams->biasOmegaCovariance = I_3x3 * pow(gyrBiasRwSigma, 2);
imuPreintegratedParams->integrationCovariance = I_3x3 * integrationCovariance;
imuPreintegratedParams->biasAccOmegaInt = I_6x6 * biasAccOmegaInt;
// Initial state
Pose3 priorPose;
Vector3 priorVelocity(0, 0, 0);
imuBias::ConstantBias priorImuBias;
PreintegratedCombinedMeasurements imuPreintegrated;
Vector3 position(0, 0, 0);
Vector3 velocity(0, 0, 0);
NavState propState;
NavState initialNavState(priorPose, priorVelocity);
// Assume zero bias for simulated data
priorImuBias =
imuBias::ConstantBias(Eigen::Vector3d(0, 0, 0), Eigen::Vector3d(0, 0, 0));
imuPreintegrated =
PreintegratedCombinedMeasurements(imuPreintegratedParams, priorImuBias);
// start at 1 to skip header
for (size_t n = 1; n < imuMeasurements.size(); n++) {
// integrate
imuPreintegrated.integrateMeasurement(imuMeasurements[n].I_a_WI,
imuMeasurements[n].I_w_WI, 1 / rate);
// predict
propState = imuPreintegrated.predict(initialNavState, priorImuBias);
position = propState.pose().translation();
velocity = propState.velocity();
}
Vector3 rotation = propState.pose().rotation().rpy();
// Dont have ground truth for x and y position yet
// DOUBLES_EQUAL(0.1, position[0], 1e-2);
// DOUBLES_EQUAL(0.1, position[1], 1e-2);
DOUBLES_EQUAL(0.0, position[2], 1e-2);
DOUBLES_EQUAL(0.0, rotation[0], 1e-2);
DOUBLES_EQUAL(0.0, rotation[1], 1e-2);
DOUBLES_EQUAL(0.0, rotation[2], 1e-2);
}
/* ************************************************************************* */
int main() {
TestResult tr;
return TestRegistry::runAllTests(tr);
}
/* ************************************************************************* */