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small changes

release/4.3a0
Frank Dellaert 2012-08-06 13:12:03 +00:00
parent b57b2df313
commit ee87b51a9f
2 changed files with 7 additions and 10 deletions
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6
matlab/gtsam_examples/PlanarSLAMExample.m
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@ -31,7 +31,7 @@ graph = NonlinearFactorGraph;
%% Add 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(i1, priorMean, priorNoise)); % add directly to graph
graph.add(PriorFactorPose2(i1, priorMean, priorNoise));
%% Add odometry
odometry = Pose2(2.0, 0.0, 0.0);
@ -42,7 +42,7 @@ graph.add(BetweenFactorPose2(i2, i3, odometry, odometryNoise));
%% Add bearing/range measurement factors
degrees = pi/180;
brNoise = noiseModel.Diagonal.Sigmas([0.1; 0.2]);
graph.add(BearingRangeFactor2D(i1, j1, Rot2(45*degrees), sqrt(4+4), brNoise));
graph.add(BearingRangeFactor2D(i1, j1, Rot2(45*degrees), sqrt(8), brNoise));
graph.add(BearingRangeFactor2D(i2, j1, Rot2(90*degrees), 2, brNoise));
graph.add(BearingRangeFactor2D(i3, j2, Rot2(90*degrees), 2, brNoise));
@ -69,7 +69,7 @@ cla;hold on
marginals = Marginals(graph, result);
plot2DTrajectory(result, [], marginals);
plot2DPoints(result, [], marginals);
plot2DPoints(result, 'b', marginals);
plot([result.at(i1).x; result.at(j1).x],[result.at(i1).y; result.at(j1).y], 'c-');
plot([result.at(i2).x; result.at(j1).x],[result.at(i2).y; result.at(j1).y], 'c-');

11
matlab/gtsam_examples/Pose2SLAMExample_graph.m
View File

@ -17,11 +17,7 @@ datafile = findExampleDataFile('w100-odom.graph');
%% Initialize graph, initial estimate, and odometry noise
model = noiseModel.Diagonal.Sigmas([0.05; 0.05; 5*pi/180]);
maxID = 0;
addNoise = false;
smart = true;
[graph,initial] = load2D(datafile, model, maxID, addNoise, smart);
initial.print(sprintf('Initial estimate:\n'));
[graph,initial] = load2D(datafile, model);
%% Add a Gaussian prior on pose x_1
priorMean = Pose2(0, 0, 0); % prior mean is at origin
@ -34,9 +30,10 @@ plot2DTrajectory(initial, 'g-*'); axis equal
%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
optimizer = LevenbergMarquardtOptimizer(graph, initial);
tic
result = optimizer.optimizeSafely;
toc
hold on; plot2DTrajectory(result, 'b-*');
result.print(sprintf('\nFinal result:\n'));
%% Plot Covariance Ellipses
marginals = Marginals(graph, result);
@ -48,4 +45,4 @@ for i=1:result.size()-1
end
view(2)
axis tight; axis equal;
fprintf(1,'%.5f %.5f %.5f\n',P{99})
% fprintf(1,'%.5f %.5f %.5f\n',P{99})