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Synchronizing C++ and MATLAB example again. Please coordinate with me before changing the values in the examples: changing them generates work in the manual and in the MATLAB examples.

release/4.3a0
Frank Dellaert 2012-08-05 18:44:36 +00:00
parent f70af2ef38
commit abdf46d494
2 changed files with 40 additions and 54 deletions
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52
examples/Pose2SLAMExample.cpp
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@ -25,14 +25,10 @@
* - 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
// In planar SLAM example we use Pose2 variables (x, y, theta) to represent the robot poses
#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
// We will use simple integer Keys to refer to the robot poses.
#include <gtsam/nonlinear/Key.h>
// In GTSAM, measurement functions are represented as 'factors'. Several common factors
@ -75,37 +71,34 @@ int main(int argc, char** argv) {
// 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(Vector_(3, 0.3, 0.3, 0.1));
graph.add(PriorFactor<Pose2>(1, prior, priorNoise));
graph.add(PriorFactor<Pose2>(1, Pose2(0, 0, 0), priorNoise));
// For simplicity, we will use the same noise model for odometry and loop closures
noiseModel::Diagonal::shared_ptr model = noiseModel::Diagonal::Sigmas(Vector_(3, 0.2, 0.2, 0.1));
// 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(Vector_(3, 0.2, 0.2, 0.1));
// Create odometry (Between) factors between consecutive poses
graph.add(BetweenFactor<Pose2>(1, 2, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
graph.add(BetweenFactor<Pose2>(2, 3, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
graph.add(BetweenFactor<Pose2>(3, 4, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
graph.add(BetweenFactor<Pose2>(4, 5, Pose2(2.0, 0.0, M_PI_2), odometryNoise));
graph.add(BetweenFactor<Pose2>(1, 2, Pose2(2, 0, 0 ), model));
graph.add(BetweenFactor<Pose2>(2, 3, Pose2(2, 0, M_PI_2), model));
graph.add(BetweenFactor<Pose2>(3, 4, Pose2(2, 0, M_PI_2), model));
graph.add(BetweenFactor<Pose2>(4, 5, Pose2(2, 0, M_PI_2), model));
// 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(Vector_(3, 0.2, 0.2, 0.1));
graph.add(BetweenFactor<Pose2>(5, 1, Pose2(0.0, 0.0, 0.0), model));
// with camera images. We will use another Between Factor to enforce this constraint:
graph.add(BetweenFactor<Pose2>(5, 2, Pose2(2, 0, M_PI_2), 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));
initialEstimate.insert(3, Pose2(2.1, 1.9, 2.8));
initialEstimate.insert(4, Pose2(-.3, 2.5, 4.2));
initialEstimate.insert(5, Pose2(0.1,-0.7, 5.8));
initialEstimate.insert(1, Pose2(0.5, 0.0, 0.2 ));
initialEstimate.insert(2, Pose2(2.3, 0.1, -0.2 ));
initialEstimate.insert(3, Pose2(4.1, 0.1, M_PI_2));
initialEstimate.insert(4, Pose2(4.0, 2.0, M_PI ));
initialEstimate.insert(5, Pose2(2.1, 2.1, -M_PI_2));
initialEstimate.print("\nInitial Estimate:\n"); // print
// 4. Optimize the initial values using a Gauss-Newton nonlinear optimizer
@ -125,12 +118,13 @@ int main(int argc, char** argv) {
result.print("Final Result:\n");
// 5. Calculate and print marginal covariances for all variables
cout.precision(3);
Marginals marginals(graph, result);
cout << "Pose 1 covariance:\n" << marginals.marginalCovariance(1) << endl;
cout << "Pose 2 covariance:\n" << marginals.marginalCovariance(2) << endl;
cout << "Pose 3 covariance:\n" << marginals.marginalCovariance(3) << endl;
cout << "Pose 4 covariance:\n" << marginals.marginalCovariance(4) << endl;
cout << "Pose 5 covariance:\n" << marginals.marginalCovariance(5) << endl;
cout << "x1 covariance:\n" << marginals.marginalCovariance(1) << endl;
cout << "x2 covariance:\n" << marginals.marginalCovariance(2) << endl;
cout << "x3 covariance:\n" << marginals.marginalCovariance(3) << endl;
cout << "x4 covariance:\n" << marginals.marginalCovariance(4) << endl;
cout << "x5 covariance:\n" << marginals.marginalCovariance(5) << endl;
return 0;
}

42
matlab/gtsam_examples/Pose2SLAMExample.m
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@ -18,58 +18,50 @@
% - We have full odometry for measurements
% - The robot is on a grid, moving 2 meters each step
import gtsam.*
%% Create graph container and add factors to it
graph = NonlinearFactorGraph;
%% Add prior
import gtsam.*
% gaussian for prior
priorMean = Pose2(0.0, 0.0, 0.0); % prior at origin
priorNoise = noiseModel.Diagonal.Sigmas([0.3; 0.3; 0.1]);
graph.add(PriorFactorPose2(1, priorMean, priorNoise)); % add directly to graph
graph.add(PriorFactorPose2(1, Pose2(0, 0, 0), priorNoise)); % add directly to graph
%% Add odometry
import gtsam.*
% general noisemodel for odometry
odometryNoise = noiseModel.Diagonal.Sigmas([0.2; 0.2; 0.1]);
graph.add(BetweenFactorPose2(1, 2, Pose2(2.0, 0.0, 0.0 ), odometryNoise));
graph.add(BetweenFactorPose2(2, 3, Pose2(2.0, 0.0, pi/2), odometryNoise));
graph.add(BetweenFactorPose2(3, 4, Pose2(2.0, 0.0, pi/2), odometryNoise));
graph.add(BetweenFactorPose2(4, 5, Pose2(2.0, 0.0, pi/2), odometryNoise));
model = noiseModel.Diagonal.Sigmas([0.2; 0.2; 0.1]);
graph.add(BetweenFactorPose2(1, 2, Pose2(2, 0, 0 ), model));
graph.add(BetweenFactorPose2(2, 3, Pose2(2, 0, pi/2), model));
graph.add(BetweenFactorPose2(3, 4, Pose2(2, 0, pi/2), model));
graph.add(BetweenFactorPose2(4, 5, Pose2(2, 0, pi/2), model));
%% Add pose constraint
import gtsam.*
model = noiseModel.Diagonal.Sigmas([0.2; 0.2; 0.1]);
graph.add(BetweenFactorPose2(5, 2, Pose2(2.0, 0.0, pi/2), model));
graph.add(BetweenFactorPose2(5, 2, Pose2(2, 0, pi/2), model));
% print
graph.print(sprintf('\nFactor graph:\n'));
%% Initialize to noisy points
import gtsam.*
initialEstimate = Values;
initialEstimate.insert(1, Pose2(0.5, 0.0, 0.2 ));
initialEstimate.insert(2, Pose2(2.3, 0.1,-0.2 ));
initialEstimate.insert(3, Pose2(4.1, 0.1, pi/2));
initialEstimate.insert(4, Pose2(4.0, 2.0, pi ));
initialEstimate.insert(5, Pose2(2.1, 2.1,-pi/2));
initialEstimate.insert(1, Pose2(0.5, 0.0, 0.2 ));
initialEstimate.insert(2, Pose2(2.3, 0.1, -0.2 ));
initialEstimate.insert(3, Pose2(4.1, 0.1, pi/2));
initialEstimate.insert(4, Pose2(4.0, 2.0, pi ));
initialEstimate.insert(5, Pose2(2.1, 2.1, -pi/2));
initialEstimate.print(sprintf('\nInitial estimate:\n'));
%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
import gtsam.*
optimizer = LevenbergMarquardtOptimizer(graph, initialEstimate);
result = optimizer.optimizeSafely();
result.print(sprintf('\nFinal result:\n'));
%% Plot Covariance Ellipses
import gtsam.*
cla;
hold on
plot([result.at(5).x;result.at(2).x],[result.at(5).y;result.at(2).y],'r-','LineWidth',2);
plot([result.at(5).x;result.at(2).x],[result.at(5).y;result.at(2).y],'r-');
marginals = Marginals(graph, result);
gtsam.plot2DTrajectory(result, [], marginals);
axis([-0.6 4.8 -1 1])
for i=1:5,marginals.marginalCovariance(i),end
axis equal
axis tight
view(2)