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Cleaned up comments and headers in examples

release/4.3a0
dellaert 2015-02-18 12:02:33 +01:00
parent 5e568bc29d
commit 9f51aad0fc
6 changed files with 35 additions and 106 deletions
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10
examples/Pose2SLAMExampleExpressions.cpp
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@ -14,20 +14,18 @@
* @brief Expressions version of Pose2SLAMExample.cpp
* @date Oct 2, 2014
* @author Frank Dellaert
* @author Yong Dian Jian
*/
// The two new headers that allow using our Automatic Differentiation Expression framework
#include <gtsam/slam/expressions.h>
#include <gtsam/nonlinear/ExpressionFactorGraph.h>
// Header order is close to far
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
// For an explanation of headers below, please see Pose2SLAMExample.cpp
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/geometry/Pose2.h>
#include <gtsam/nonlinear/GaussNewtonOptimizer.h>
#include <gtsam/nonlinear/Marginals.h>
#include <gtsam/nonlinear/Values.h>
#include <gtsam/geometry/Pose2.h>
#include <gtsam/inference/Key.h>
using namespace std;
using namespace gtsam;

9
examples/Pose2SLAMExample_graph.cpp
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@ -16,11 +16,14 @@
* @author Frank Dellaert
*/
#include <gtsam/slam/dataset.h>
// For an explanation of headers below, please see Pose2SLAMExample.cpp
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/nonlinear/Marginals.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/geometry/Pose2.h>
#include <gtsam/nonlinear/Marginals.h>
// This new header allows us to read examples easily from .graph files
#include <gtsam/slam/dataset.h>
using namespace std;
using namespace gtsam;

4
examples/Pose2SLAMExample_graphviz.cpp
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@ -16,11 +16,11 @@
* @author Frank Dellaert
*/
// For an explanation of headers below, please see Pose2SLAMExample.cpp
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/nonlinear/Marginals.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/geometry/Pose2.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <fstream>
using namespace std;

59
examples/Pose2SLAMwSPCG.cpp
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@ -16,47 +16,15 @@
* @date June 2, 2012
*/
/**
* A simple 2D pose slam example solved using a Conjugate-Gradient method
* - The robot moves in a 2 meter square
* - The robot moves 2 meters each step, turning 90 degrees after each step
* - The robot initially faces along the X axis (horizontal, to the right in 2D)
* - We have full odometry between pose
* - We have a loop closure constraint when the robot returns to the first position
*/
// As this is a planar SLAM example, we will use Pose2 variables (x, y, theta) to represent
// the robot positions
#include <gtsam/geometry/Pose2.h>
#include <gtsam/geometry/Point2.h>
// Each variable in the system (poses) must be identified with a unique key.
// We can either use simple integer keys (1, 2, 3, ...) or symbols (X1, X2, L1).
// Here we will use simple integer keys
#include <gtsam/inference/Key.h>
// In GTSAM, measurement functions are represented as 'factors'. Several common factors
// have been provided with the library for solving robotics/SLAM/Bundle Adjustment problems.
// Here we will use Between factors for the relative motion described by odometry measurements.
// We will also use a Between Factor to encode the loop closure constraint
// Also, we will initialize the robot at the origin using a Prior factor.
// For an explanation of headers below, please see Pose2SLAMExample.cpp
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/BetweenFactor.h>
// When the factors are created, we will add them to a Factor Graph. As the factors we are using
// are nonlinear factors, we will need a Nonlinear Factor Graph.
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
// The nonlinear solvers within GTSAM are iterative solvers, meaning they linearize the
// nonlinear functions around an initial linearization point, then solve the linear system
// to update the linearization point. This happens repeatedly until the solver converges
// to a consistent set of variable values. This requires us to specify an initial guess
// for each variable, held in a Values container.
#include <gtsam/nonlinear/Values.h>
#include <gtsam/linear/SubgraphSolver.h>
#include <gtsam/geometry/Pose2.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
// In contrast to that example, however, we will use a PCG solver here
#include <gtsam/linear/SubgraphSolver.h>
using namespace std;
using namespace gtsam;
@ -66,32 +34,24 @@ int main(int argc, char** argv) {
NonlinearFactorGraph graph;
// 2a. Add a prior on the first pose, setting it to the origin
// A prior factor consists of a mean and a noise model (covariance matrix)
Pose2 prior(0.0, 0.0, 0.0); // prior at origin
noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector3(0.3, 0.3, 0.1));
graph.push_back(PriorFactor<Pose2>(1, prior, priorNoise));
// 2b. Add odometry factors
// For simplicity, we will use the same noise model for each odometry factor
noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
// Create odometry (Between) factors between consecutive poses
graph.push_back(BetweenFactor<Pose2>(1, 2, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
graph.push_back(BetweenFactor<Pose2>(2, 3, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
graph.push_back(BetweenFactor<Pose2>(3, 4, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
graph.push_back(BetweenFactor<Pose2>(4, 5, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
// 2c. Add the loop closure constraint
// This factor encodes the fact that we have returned to the same pose. In real systems,
// these constraints may be identified in many ways, such as appearance-based techniques
// with camera images.
// We will use another Between Factor to enforce this constraint, with the distance set to zero,
noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas(Vector3(0.2, 0.2, 0.1));
graph.push_back(BetweenFactor<Pose2>(5, 1, Pose2(0.0, 0.0, 0.0), model));
graph.print("\nFactor Graph:\n"); // print
// 3. Create the data structure to hold the initialEstimate estimate to the solution
// For illustrative purposes, these have been deliberately set to incorrect values
Values initialEstimate;
initialEstimate.insert(1, Pose2(0.5, 0.0, 0.2));
initialEstimate.insert(2, Pose2(2.3, 0.1, 1.1));
@ -104,15 +64,18 @@ int main(int argc, char** argv) {
LevenbergMarquardtParams parameters;
parameters.verbosity = NonlinearOptimizerParams::ERROR;
parameters.verbosityLM = LevenbergMarquardtParams::LAMBDA;
parameters.linearSolverType = NonlinearOptimizerParams::Iterative;
{
// LM is still the outer optimization loop, but by specifying "Iterative" below
// We indicate that an iterative linear solver should be used.
// In addition, the *type* of the iterativeParams decides on the type of
// iterative solver, in this case the SPCG (subgraph PCG)
parameters.linearSolverType = NonlinearOptimizerParams::Iterative;
parameters.iterativeParams = boost::make_shared<SubgraphSolverParameters>();
LevenbergMarquardtOptimizer optimizer(graph, initialEstimate, parameters);
Values result = optimizer.optimize();
result.print("Final Result:\n");
cout << "subgraph solver final error = " << graph.error(result) << endl;
}
return 0;
}

8
examples/SFMExample.cpp
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@ -15,13 +15,7 @@
* @author Duy-Nguyen Ta
*/
/**
* A structure-from-motion example with landmarks
* - The landmarks form a 10 meter cube
* - The robot rotates around the landmarks, always facing towards the cube
*/
// For loading the data
// For loading the data, see the comments therein for scenario (camera rotates around cube)
#include "SFMdata.h"
// Camera observations of landmarks (i.e. pixel coordinates) will be stored as Point2 (x, y).

41
examples/SFMExample_SmartFactor.cpp
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@ -17,46 +17,17 @@
* @author Frank Dellaert
*/
/**
* A structure-from-motion example with landmarks
* - The landmarks form a 10 meter cube
* - The robot rotates around the landmarks, always facing towards the cube
*/
// For loading the data
#include "SFMdata.h"
// Camera observations of landmarks (i.e. pixel coordinates) will be stored as Point2 (x, y).
#include <gtsam/geometry/Point2.h>
// In GTSAM, measurement functions are represented as 'factors'.
// The factor we used here is SmartProjectionPoseFactor. Every smart factor represent a single landmark,
// The SmartProjectionPoseFactor only optimize the pose of camera, not the calibration,
// The calibration should be known.
// The factor we used here is SmartProjectionPoseFactor.
// Every smart factor represent a single landmark, seen from multiple cameras.
// The SmartProjectionPoseFactor only optimizes for the poses of a camera,
// not the calibration, which is assumed known.
#include <gtsam/slam/SmartProjectionPoseFactor.h>
// Also, we will initialize the robot at some location using a Prior factor.
#include <gtsam/slam/PriorFactor.h>
// When the factors are created, we will add them to a Factor Graph. As the factors we are using
// are nonlinear factors, we will need a Nonlinear Factor Graph.
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
// Finally, once all of the factors have been added to our factor graph, we will want to
// solve/optimize to graph to find the best (Maximum A Posteriori) set of variable values.
// GTSAM includes several nonlinear optimizers to perform this step. Here we will use a
// trust-region method known as Powell's Degleg
// For an explanation of these headers, see SFMExample.cpp
#include "SFMdata.h"
#include <gtsam/nonlinear/DoglegOptimizer.h>
// The nonlinear solvers within GTSAM are iterative solvers, meaning they linearize the
// nonlinear functions around an initial linearization point, then solve the linear system
// to update the linearization point. This happens repeatedly until the solver converges
// to a consistent set of variable values. This requires us to specify an initial guess
// for each variable, held in a Values container.
#include <gtsam/nonlinear/Values.h>
#include <vector>
using namespace std;
using namespace gtsam;