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Combined the PlanarSLAM examples into a single example without SLAM namespaces

release/4.3a0
Stephen Williams 2012-07-22 04:36:40 +00:00
parent d259320aed
commit 5da5adb2f1
2 changed files with 94 additions and 181 deletions
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138
examples/PlanarSLAMExample.cpp
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@ -11,22 +11,12 @@
/** /**
* @file PlanarSLAMExample.cpp * @file PlanarSLAMExample.cpp
* @brief Simple robotics example using the pre-built planar SLAM domain * @brief Simple robotics example using odometry measurements and bearing-range (laser) measurements
* @author Alex Cunningham * @author Alex Cunningham
*/ */
// pull in the planar SLAM domain with all typedefs and helper functions defined
#include <gtsam/slam/planarSLAM.h>
// we will use Symbol keys
#include <gtsam/nonlinear/Symbol.h>
using namespace std;
using namespace gtsam;
using namespace gtsam::noiseModel;
/** /**
* Example of a simple 2D planar slam example with landmarls * A simple 2D planar slam example with landmarks
* - The robot and landmarks are on a 2 meter grid * - The robot and landmarks are on a 2 meter grid
* - Robot poses are facing along the X axis (horizontal, to the right in 2D) * - Robot poses are facing along the X axis (horizontal, to the right in 2D)
* - The robot moves 2 meters each step * - The robot moves 2 meters each step
@ -34,29 +24,74 @@ using namespace gtsam::noiseModel;
* - We have bearing and range information for measurements * - We have bearing and range information for measurements
* - Landmarks are 2 meters away from the robot trajectory * - Landmarks are 2 meters away from the robot trajectory
*/ */
// As this is a planar SLAM example, we will use Pose2 variables (x, y, theta) to represent
// the robot positions and Point2 variables (x, y) to represent the landmark coordinates.
#include <gtsam/geometry/Pose2.h>
#include <gtsam/geometry/Point2.h>
// Each variable in the system (poses and landmarks) 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 Symbols
#include <gtsam/nonlinear/Symbol.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 a RangeBearing factor for the range-bearing measurements to identified
// landmarks, and Between factors for the relative motion described by odometry measurements.
// Also, we will initialize the robot at the origin using a Prior factor.
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/slam/BearingRangeFactor.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 the
// common Levenberg-Marquardt solver
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
// Once the optimized values have been calculated, we can also calculate the marginal covariance
// of desired variables
#include <gtsam/nonlinear/Marginals.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>
using namespace std;
using namespace gtsam;
int main(int argc, char** argv) { int main(int argc, char** argv) {
// create the graph (defined in planarSlam.h, derived from NonlinearFactorGraph) // Create a factor graph
planarSLAM::Graph graph; NonlinearFactorGraph graph;
// Create some keys // Create the keys we need for this simple example
static Symbol i1('x',1), i2('x',2), i3('x',3); static Symbol x1('x',1), x2('x',2), x3('x',3);
static Symbol j1('l',1), j2('l',2); static Symbol l1('l',1), l2('l',2);
// add a Gaussian prior on pose x_1 // Add a prior on pose x1 at the origin. A prior factor consists of a mean and a noise model (covariance matrix)
Pose2 priorMean(0.0, 0.0, 0.0); // prior mean is at origin Pose2 prior(0.0, 0.0, 0.0); // prior mean is at origin
SharedDiagonal priorNoise = Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1)); // 30cm std on x,y, 0.1 rad on theta noiseModel::Diagonal::shared_ptr priorNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1)); // 30cm std on x,y, 0.1 rad on theta
graph.addPosePrior(i1, priorMean, priorNoise); // add directly to graph graph.add(PriorFactor<Pose2>(x1, prior, priorNoise)); // add directly to graph
// add two odometry factors // Add two odometry factors
Pose2 odometry(2.0, 0.0, 0.0); // create a measurement for both factors (the same in this case) Pose2 odometry(2.0, 0.0, 0.0); // create a measurement for both factors (the same in this case)
SharedDiagonal odometryNoise = Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1)); // 20cm std on x,y, 0.1 rad on theta noiseModel::Diagonal::shared_ptr odometryNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1)); // 20cm std on x,y, 0.1 rad on theta
graph.addRelativePose(i1, i2, odometry, odometryNoise); graph.add(BetweenFactor<Pose2>(x1, x2, odometry, odometryNoise));
graph.addRelativePose(i2, i3, odometry, odometryNoise); graph.add(BetweenFactor<Pose2>(x2, x3, odometry, odometryNoise));
// Add Range-Bearing measurements to two different landmarks
// create a noise model for the landmark measurements // create a noise model for the landmark measurements
SharedDiagonal measurementNoise = Diagonal::Sigmas(Vector_(2, 0.1, 0.2)); // 0.1 rad std on bearing, 20cm on range noiseModel::Diagonal::shared_ptr measurementNoise = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.2)); // 0.1 rad std on bearing, 20cm on range
// create the measurement values - indices are (pose id, landmark id) // create the measurement values - indices are (pose id, landmark id)
Rot2 bearing11 = Rot2::fromDegrees(45), Rot2 bearing11 = Rot2::fromDegrees(45),
bearing21 = Rot2::fromDegrees(90), bearing21 = Rot2::fromDegrees(90),
@ -65,27 +100,42 @@ int main(int argc, char** argv) {
range21 = 2.0, range21 = 2.0,
range32 = 2.0; range32 = 2.0;
// add bearing/range factors (created by "addBearingRange") // Add Bearing-Range factors
graph.addBearingRange(i1, j1, bearing11, range11, measurementNoise); graph.add(BearingRangeFactor<Pose2, Point2>(x1, l1, bearing11, range11, measurementNoise));
graph.addBearingRange(i2, j1, bearing21, range21, measurementNoise); graph.add(BearingRangeFactor<Pose2, Point2>(x2, l1, bearing21, range21, measurementNoise));
graph.addBearingRange(i3, j2, bearing32, range32, measurementNoise); graph.add(BearingRangeFactor<Pose2, Point2>(x3, l2, bearing32, range32, measurementNoise));
// print // Print
graph.print("Factor graph"); graph.print("Factor Graph:\n");
// create (deliberatly inaccurate) initial estimate // Create (deliberately inaccurate) initial estimate
planarSLAM::Values initialEstimate; Values initialEstimate;
initialEstimate.insertPose(i1, Pose2(0.5, 0.0, 0.2)); initialEstimate.insert(x1, Pose2(0.5, 0.0, 0.2));
initialEstimate.insertPose(i2, Pose2(2.3, 0.1,-0.2)); initialEstimate.insert(x2, Pose2(2.3, 0.1,-0.2));
initialEstimate.insertPose(i3, Pose2(4.1, 0.1, 0.1)); initialEstimate.insert(x3, Pose2(4.1, 0.1, 0.1));
initialEstimate.insertPoint(j1, Point2(1.8, 2.1)); initialEstimate.insert(l1, Point2(1.8, 2.1));
initialEstimate.insertPoint(j2, Point2(4.1, 1.8)); initialEstimate.insert(l2, Point2(4.1, 1.8));
initialEstimate.print("Initial estimate:\n "); // Print
initialEstimate.print("Initial Estimate:\n");
// optimize using Levenberg-Marquardt optimization with an ordering from colamd // Optimize using Levenberg-Marquardt optimization. The optimizer
planarSLAM::Values result = graph.optimize(initialEstimate); // accepts an optional set of configuration parameters, controlling
result.print("Final result:\n "); // things like convergence criteria, the type of linear system solver
// to use, and the amount of information displayed during optimization.
// Here we will use the default set of parameters. See the
// documentation for the full set of parameters.
LevenbergMarquardtOptimizer optimizer(graph, initialEstimate);
Values result = optimizer.optimize();
result.print("Final Result:\n");
// Calculate and print marginal covariances for all variables
Marginals marginals(graph, result);
print(marginals.marginalCovariance(x1), "x1 covariance");
print(marginals.marginalCovariance(x2), "x2 covariance");
print(marginals.marginalCovariance(x3), "x3 covariance");
print(marginals.marginalCovariance(l1), "l1 covariance");
print(marginals.marginalCovariance(l2), "l2 covariance");
return 0; return 0;
} }

137
examples/PlanarSLAMExample_selfcontained.cpp
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@ -1,137 +0,0 @@
/* ----------------------------------------------------------------------------
* 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 PlanarSLAMExample_selfcontained.cpp
* @brief Simple robotics example with all typedefs internal to this script.
* @author Alex Cunningham
*/
// add in headers for specific factors
#include <gtsam/slam/PriorFactor.h>
#include <gtsam/slam/BetweenFactor.h>
#include <gtsam/slam/BearingRangeFactor.h>
// for all nonlinear keys
#include <gtsam/nonlinear/Symbol.h>
// implementations for structures - needed if self-contained, and these should be included last
#include <gtsam/nonlinear/NonlinearFactorGraph.h>
#include <gtsam/nonlinear/LevenbergMarquardtOptimizer.h>
#include <gtsam/nonlinear/Marginals.h>
// for modeling measurement uncertainty - all models included here
#include <gtsam/linear/NoiseModel.h>
// for points and poses
#include <gtsam/geometry/Point2.h>
#include <gtsam/geometry/Pose2.h>
#include <cmath>
#include <iostream>
using namespace std;
using namespace gtsam;
/**
* In this version of the system we make the following assumptions:
* - All values are axis aligned
* - Robot poses are facing along the X axis (horizontal, to the right in images)
* - We have bearing and range information for measurements
* - We have full odometry for measurements
* - The robot and landmarks are on a grid, moving 2 meters each step
* - Landmarks are 2 meters away from the robot trajectory
*/
int main(int argc, char** argv) {
// create keys for variables
Symbol i1('x',1), i2('x',2), i3('x',3);
Symbol j1('l',1), j2('l',2);
// create graph container and add factors to it
NonlinearFactorGraph graph;
/* add prior */
// gaussian for prior
SharedDiagonal priorNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.3, 0.3, 0.1));
Pose2 priorMean(0.0, 0.0, 0.0); // prior at origin
PriorFactor<Pose2> posePrior(i1, priorMean, priorNoise); // create the factor
graph.add(posePrior); // add the factor to the graph
/* add odometry */
// general noisemodel for odometry
SharedDiagonal odometryNoise = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
Pose2 odometry(2.0, 0.0, 0.0); // create a measurement for both factors (the same in this case)
// create between factors to represent odometry
BetweenFactor<Pose2> odom12(i1, i2, odometry, odometryNoise);
BetweenFactor<Pose2> odom23(i2, i3, odometry, odometryNoise);
graph.add(odom12); // add both to graph
graph.add(odom23);
/* add measurements */
// general noisemodel for measurements
SharedDiagonal measurementNoise = noiseModel::Diagonal::Sigmas(Vector_(2, 0.1, 0.2));
// create the measurement values - indices are (pose id, landmark id)
Rot2 bearing11 = Rot2::fromDegrees(45),
bearing21 = Rot2::fromDegrees(90),
bearing32 = Rot2::fromDegrees(90);
double range11 = sqrt(4.0+4.0),
range21 = 2.0,
range32 = 2.0;
// create bearing/range factors
BearingRangeFactor<Pose2, Point2> meas11(i1, j1, bearing11, range11, measurementNoise);
BearingRangeFactor<Pose2, Point2> meas21(i2, j1, bearing21, range21, measurementNoise);
BearingRangeFactor<Pose2, Point2> meas32(i3, j2, bearing32, range32, measurementNoise);
// add the factors
graph.add(meas11);
graph.add(meas21);
graph.add(meas32);
graph.print("Full Graph");
// initialize to noisy points
Values initial;
initial.insert(i1, Pose2(0.5, 0.0, 0.2));
initial.insert(i2, Pose2(2.3, 0.1,-0.2));
initial.insert(i3, Pose2(4.1, 0.1, 0.1));
initial.insert(j1, Point2(1.8, 2.1));
initial.insert(j2, Point2(4.1, 1.8));
initial.print("initial estimate");
// optimize using Levenberg-Marquardt optimization with an ordering from colamd
// first using sequential elimination
LevenbergMarquardtParams lmParams;
lmParams.linearSolverType = LevenbergMarquardtParams::SEQUENTIAL_CHOLESKY;
Values resultSequential = LevenbergMarquardtOptimizer(graph, initial, lmParams).optimize();
resultSequential.print("final result (solved with a sequential solver)");
// then using multifrontal, advanced interface
// Note that we keep the original optimizer object so we can use the COLAMD
// ordering it computes.
LevenbergMarquardtOptimizer optimizer(graph, initial);
Values resultMultifrontal = optimizer.optimize();
resultMultifrontal.print("final result (solved with a multifrontal solver)");
// Print marginals covariances for all variables
Marginals marginals(graph, resultMultifrontal, Marginals::CHOLESKY);
print(marginals.marginalCovariance(i1), "i1 covariance");
print(marginals.marginalCovariance(i2), "i2 covariance");
print(marginals.marginalCovariance(i3), "i3 covariance");
print(marginals.marginalCovariance(j1), "j1 covariance");
print(marginals.marginalCovariance(j2), "j2 covariance");
return 0;
}