Merge branch 'develop' into python-updates
						commit
						f7748f71b9
					
				|  | @ -1,11 +1,5 @@ | |||
| cmake_minimum_required(VERSION 3.0) | ||||
| 
 | ||||
| # new feature to Cmake Version > 2.8.12 | ||||
| # Mac ONLY. Define Relative Path on Mac OS | ||||
| if(NOT DEFINED CMAKE_MACOSX_RPATH) | ||||
|   set(CMAKE_MACOSX_RPATH 0) | ||||
| endif() | ||||
| 
 | ||||
| # Set the version number for the library | ||||
| set (GTSAM_VERSION_MAJOR 4) | ||||
| set (GTSAM_VERSION_MINOR 3) | ||||
|  |  | |||
|  | @ -182,7 +182,7 @@ Here are some tips to get the best possible performance out of GTSAM. | |||
|     optimization by 30-50%. Please note that this may not be true for very small  | ||||
|     problems where the overhead of dispatching work to multiple threads outweighs | ||||
|     the benefit. We recommend that you benchmark your problem with/without TBB. | ||||
| 3. Add `-march=native` to `GTSAM_CMAKE_CXX_FLAGS`. A performance gain of | ||||
| 3. Use `GTSAM_BUILD_WITH_MARCH_NATIVE`. A performance gain of | ||||
|     25-30% can be expected on modern processors. Note that this affects the portability | ||||
|     of your executable. It may not run when copied to another system with older/different | ||||
|     processor architecture. | ||||
|  |  | |||
|  | @ -56,7 +56,7 @@ if (GTSAM_BUILD_DOCS) | |||
|     if (GTSAM_BUILD_UNSTABLE) | ||||
|         list(APPEND doc_subdirs ${gtsam_unstable_doc_subdirs}) | ||||
|     endif() | ||||
|     if (GTSAM_BUILD_EXAMPLES) | ||||
|     if (GTSAM_BUILD_EXAMPLES_ALWAYS) | ||||
|         list(APPEND doc_subdirs examples) | ||||
|     endif() | ||||
|      | ||||
|  |  | |||
|  | @ -0,0 +1,75 @@ | |||
| /* ----------------------------------------------------------------------------
 | ||||
| 
 | ||||
|  * 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 GNCExample.cpp | ||||
|  * @brief Simple example showcasing a Graduated Non-Convexity based solver | ||||
|  * @author Achintya Mohan | ||||
|  */ | ||||
| 
 | ||||
| /**
 | ||||
|  * A simple 2D pose graph optimization example | ||||
|  * - The robot is initially at origin (0.0, 0.0, 0.0)  | ||||
|  * - We have full odometry measurements for 2 motions | ||||
|  * - The robot first moves to (1.0, 0.0, 0.1) and then to (1.0, 1.0, 0.2)  | ||||
|  */ | ||||
| 
 | ||||
| #include <gtsam/geometry/Pose2.h> | ||||
| #include <gtsam/nonlinear/GncOptimizer.h> | ||||
| #include <gtsam/nonlinear/GncParams.h> | ||||
| #include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h> | ||||
| #include <gtsam/nonlinear/LevenbergMarquardtParams.h> | ||||
| #include <gtsam/nonlinear/NonlinearFactorGraph.h> | ||||
| #include <gtsam/slam/BetweenFactor.h> | ||||
| 
 | ||||
| #include <iostream> | ||||
| 
 | ||||
| using namespace std; | ||||
| using namespace gtsam; | ||||
| 
 | ||||
| int main() { | ||||
|   cout << "Graduated Non-Convexity Example\n"; | ||||
| 
 | ||||
|   NonlinearFactorGraph graph; | ||||
| 
 | ||||
|   // Add a prior to the first point, set to the origin
 | ||||
|   auto priorNoise = noiseModel::Isotropic::Sigma(3, 0.1); | ||||
|   graph.addPrior(1, Pose2(0.0, 0.0, 0.0), priorNoise); | ||||
| 
 | ||||
|   // Add additional factors, noise models must be Gaussian 
 | ||||
|   Pose2 x1(1.0, 0.0, 0.1); | ||||
|   graph.emplace_shared<BetweenFactor<Pose2>>(1, 2, x1, noiseModel::Isotropic::Sigma(3, 0.2)); | ||||
|   Pose2 x2(0.0, 1.0, 0.1); | ||||
|   graph.emplace_shared<BetweenFactor<Pose2>>(2, 3, x2, noiseModel::Isotropic::Sigma(3, 0.4)); | ||||
| 
 | ||||
|   // Initial estimates
 | ||||
|   Values initial; | ||||
|   initial.insert(1, Pose2(0.2, 0.5, -0.1)); | ||||
|   initial.insert(2, Pose2(0.8, 0.3, 0.1)); | ||||
|   initial.insert(3, Pose2(0.8, 0.2, 0.3)); | ||||
| 
 | ||||
|   // Set options for the non-minimal solver
 | ||||
|   LevenbergMarquardtParams lmParams; | ||||
|   lmParams.setMaxIterations(1000); | ||||
|   lmParams.setRelativeErrorTol(1e-5); | ||||
| 
 | ||||
|   // Set GNC-specific options
 | ||||
|   GncParams<LevenbergMarquardtParams> gncParams(lmParams); | ||||
|   gncParams.setLossType(GncLossType::TLS); | ||||
| 
 | ||||
|   // Optimize the graph and print results
 | ||||
|   GncOptimizer<GncParams<LevenbergMarquardtParams>> optimizer(graph, initial, gncParams); | ||||
|   Values result = optimizer.optimize(); | ||||
|   result.print("Final Result:"); | ||||
| 
 | ||||
|   return 0; | ||||
| } | ||||
| 
 | ||||
|  | @ -280,11 +280,11 @@ class DiscreteLookupDAG { | |||
| }; | ||||
| 
 | ||||
| #include <gtsam/discrete/DiscreteFactorGraph.h> | ||||
| std::pair<gtsam::DiscreteConditional*, gtsam::DecisionTreeFactor*> | ||||
| pair<gtsam::DiscreteConditional*, gtsam::DecisionTreeFactor*> | ||||
| EliminateDiscrete(const gtsam::DiscreteFactorGraph& factors, | ||||
|                   const gtsam::Ordering& frontalKeys); | ||||
| 
 | ||||
| std::pair<gtsam::DiscreteConditional*, gtsam::DecisionTreeFactor*> | ||||
| pair<gtsam::DiscreteConditional*, gtsam::DecisionTreeFactor*> | ||||
| EliminateForMPE(const gtsam::DiscreteFactorGraph& factors, | ||||
|                 const gtsam::Ordering& frontalKeys); | ||||
| 
 | ||||
|  |  | |||
|  | @ -24,6 +24,13 @@ | |||
| #include <cmath> | ||||
| #include <iostream> | ||||
| 
 | ||||
| #if defined(__i686__) || defined(__i386__) | ||||
| // See issue discussion: https://github.com/borglab/gtsam/issues/1605
 | ||||
| constexpr double TEST_THRESHOLD = 1e-5; | ||||
| #else | ||||
| constexpr double TEST_THRESHOLD = 1e-7; | ||||
| #endif | ||||
| 
 | ||||
| using namespace std::placeholders; | ||||
| using namespace std; | ||||
| using namespace gtsam; | ||||
|  | @ -104,8 +111,8 @@ TEST(SphericalCamera, Dproject) { | |||
|   Matrix numerical_pose = numericalDerivative21(project3, pose, point1); | ||||
|   Matrix numerical_point = numericalDerivative22(project3, pose, point1); | ||||
|   EXPECT(assert_equal(bearing1, result)); | ||||
|   EXPECT(assert_equal(numerical_pose, Dpose, 1e-7)); | ||||
|   EXPECT(assert_equal(numerical_point, Dpoint, 1e-7)); | ||||
|   EXPECT(assert_equal(numerical_pose, Dpose, TEST_THRESHOLD)); | ||||
|   EXPECT(assert_equal(numerical_point, Dpoint, TEST_THRESHOLD)); | ||||
| } | ||||
| 
 | ||||
| /* ************************************************************************* */ | ||||
|  | @ -123,8 +130,8 @@ TEST(SphericalCamera, reprojectionError) { | |||
|   Matrix numerical_point = | ||||
|       numericalDerivative32(reprojectionError2, pose, point1, bearing1); | ||||
|   EXPECT(assert_equal(Vector2(0.0, 0.0), result)); | ||||
|   EXPECT(assert_equal(numerical_pose, Dpose, 1e-7)); | ||||
|   EXPECT(assert_equal(numerical_point, Dpoint, 1e-7)); | ||||
|   EXPECT(assert_equal(numerical_pose, Dpose, TEST_THRESHOLD)); | ||||
|   EXPECT(assert_equal(numerical_point, Dpoint, TEST_THRESHOLD)); | ||||
| } | ||||
| 
 | ||||
| /* ************************************************************************* */ | ||||
|  | @ -137,9 +144,9 @@ TEST(SphericalCamera, reprojectionError_noisy) { | |||
|       numericalDerivative31(reprojectionError2, pose, point1, bearing_noisy); | ||||
|   Matrix numerical_point = | ||||
|       numericalDerivative32(reprojectionError2, pose, point1, bearing_noisy); | ||||
|   EXPECT(assert_equal(Vector2(-0.050282, 0.00833482), result, 1e-5)); | ||||
|   EXPECT(assert_equal(numerical_pose, Dpose, 1e-7)); | ||||
|   EXPECT(assert_equal(numerical_point, Dpoint, 1e-7)); | ||||
|   EXPECT(assert_equal(Vector2(-0.050282, 0.00833482), result, 1e2*TEST_THRESHOLD)); | ||||
|   EXPECT(assert_equal(numerical_pose, Dpose, TEST_THRESHOLD)); | ||||
|   EXPECT(assert_equal(numerical_point, Dpoint, TEST_THRESHOLD)); | ||||
| } | ||||
| 
 | ||||
| /* ************************************************************************* */ | ||||
|  | @ -151,8 +158,8 @@ TEST(SphericalCamera, Dproject2) { | |||
|   camera.project2(point1, Dpose, Dpoint); | ||||
|   Matrix numerical_pose = numericalDerivative21(project3, pose1, point1); | ||||
|   Matrix numerical_point = numericalDerivative22(project3, pose1, point1); | ||||
|   CHECK(assert_equal(numerical_pose, Dpose, 1e-7)); | ||||
|   CHECK(assert_equal(numerical_point, Dpoint, 1e-7)); | ||||
|   CHECK(assert_equal(numerical_pose, Dpose, TEST_THRESHOLD)); | ||||
|   CHECK(assert_equal(numerical_point, Dpoint, TEST_THRESHOLD)); | ||||
| } | ||||
| 
 | ||||
| /* ************************************************************************* */ | ||||
|  |  | |||
|  | @ -46,7 +46,7 @@ namespace gtsam { | |||
|  * Had to use the static_cast of a nullptr, because the compiler is not able to | ||||
|  * deduce the type of the nullptr when expanding the evaluateError templates. | ||||
|  */ | ||||
| #define OptionalNone static_cast<Matrix*>(nullptr) | ||||
| #define OptionalNone static_cast<gtsam::Matrix*>(nullptr) | ||||
| 
 | ||||
| /** This typedef will be used everywhere boost::optional<Matrix&> reference was used
 | ||||
|  * previously. This is used to indicate that the Jacobian is optional. In the future | ||||
|  |  | |||
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