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Updated matlab SFMExample

release/4.3a0
Richard Roberts 2012-07-23 22:15:08 +00:00
parent a99595dda8
commit da598b428d
1 changed files with 15 additions and 20 deletions
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35
matlab/examples/SFMExample.m
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@ -29,7 +29,8 @@ pointNoiseSigma = 0.1;
poseNoiseSigmas = [0.001 0.001 0.001 0.1 0.1 0.1]';
%% Create the graph (defined in visualSLAM.h, derived from NonlinearFactorGraph)
graph = visualSLAM.Graph;
import gtsam.*
graph = NonlinearFactorGraph;
%% Add factors for all measurements
import gtsam.*
@ -37,29 +38,30 @@ measurementNoise = noiseModel.Isotropic.Sigma(2,measurementNoiseSigma);
for i=1:length(data.Z)
for k=1:length(data.Z{i})
j = data.J{i}{k};
graph.addMeasurement(data.Z{i}{k}, measurementNoise, symbol('x',i), symbol('p',j), data.K);
graph.add(GenericProjectionFactorCal3_S2(data.Z{i}{k}, measurementNoise, symbol('x',i), symbol('p',j), data.K));
end
end
%% Add Gaussian priors for a pose and a landmark to constrain the system
import gtsam.*
posePriorNoise = noiseModel.Diagonal.Sigmas(poseNoiseSigmas);
graph.addPosePrior(symbol('x',1), truth.cameras{1}.pose, posePriorNoise);
graph.add(PriorFactorPose3(symbol('x',1), truth.cameras{1}.pose, posePriorNoise));
pointPriorNoise = noiseModel.Isotropic.Sigma(3,pointNoiseSigma);
graph.addPointPrior(symbol('p',1), truth.points{1}, pointPriorNoise);
graph.add(PriorFactorPoint3(symbol('p',1), truth.points{1}, pointPriorNoise));
%% Print the graph
graph.print(sprintf('\nFactor graph:\n'));
%% Initialize cameras and points close to ground truth in this example
initialEstimate = visualSLAM.Values;
import gtsam.*
initialEstimate = Values;
for i=1:size(truth.cameras,2)
pose_i = truth.cameras{i}.pose.retract(0.1*randn(6,1));
initialEstimate.insertPose(symbol('x',i), pose_i);
initialEstimate.insert(symbol('x',i), pose_i);
end
for j=1:size(truth.points,2)
point_j = truth.points{j}.retract(0.1*randn(3,1));
initialEstimate.insertPoint(symbol('p',j), point_j);
initialEstimate.insert(symbol('p',j), point_j);
end
initialEstimate.print(sprintf('\nInitial estimate:\n '));
@ -70,7 +72,7 @@ parameters = LevenbergMarquardtParams;
parameters.setlambdaInitial(1.0);
parameters.setVerbosityLM('trylambda');
optimizer = graph.optimizer(initialEstimate, parameters);
optimizer = LevenbergMarquardtOptimizer(graph, initialEstimate, parameters);
for i=1:5
optimizer.iterate();
@ -79,21 +81,14 @@ result = optimizer.values();
result.print(sprintf('\nFinal result:\n '));
%% Plot results with covariance ellipses
marginals = graph.marginals(result);
import gtsam.*
marginals = Marginals(graph, result);
cla
hold on;
for j=1:result.nrPoints
P = marginals.marginalCovariance(symbol('p',j));
point_j = result.point(symbol('p',j));
plot3(point_j.x, point_j.y, point_j.z,'marker','o');
covarianceEllipse3D([point_j.x;point_j.y;point_j.z],P);
end
for i=1:result.nrPoses
P = marginals.marginalCovariance(symbol('x',i));
pose_i = result.pose(symbol('x',i));
plotPose3(pose_i,P,10);
end
plot3DPoints(result, [], marginals);
plot3DTrajectory(result, '*', 1, 8, marginals);
axis([-40 40 -40 40 -10 20]);axis equal
view(3)
colormap('hot')