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Converted first 2 matlab examples to not use slam namespaces

release/4.3a0
Richard Roberts 2012-07-22 00:57:54 +00:00
parent f97869cf20
commit 9e278b394a
2 changed files with 32 additions and 40 deletions
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38
matlab/examples/LocalizationExample.m
View File

@ -16,51 +16,47 @@
% - The robot is on a grid, moving 2 meters each step
%% Create the graph (defined in pose2SLAM.h, derived from NonlinearFactorGraph)
graph = pose2SLAM.Graph;
graph = gtsam.NonlinearFactorGraph;
%% Add two odometry factors
import gtsam.*
odometry = Pose2(2.0, 0.0, 0.0); % create a measurement for both factors (the same in this case)
odometryNoise = noiseModel.Diagonal.Sigmas([0.2; 0.2; 0.1]); % 20cm std on x,y, 0.1 rad on theta
graph.addRelativePose(1, 2, odometry, odometryNoise);
graph.addRelativePose(2, 3, odometry, odometryNoise);
graph.add(BetweenFactorPose2(1, 2, odometry, odometryNoise));
graph.add(BetweenFactorPose2(2, 3, odometry, odometryNoise));
%% Add three "GPS" measurements
import gtsam.*
% We use Pose2 Priors here with high variance on theta
priorNoise = noiseModel.Diagonal.Sigmas([0.1; 0.1; 10]);
graph.addPosePrior(1, Pose2(0.0, 0.0, 0.0), priorNoise);
graph.addPosePrior(2, Pose2(2.0, 0.0, 0.0), priorNoise);
graph.addPosePrior(3, Pose2(4.0, 0.0, 0.0), priorNoise);
graph.add(PriorFactorPose2(1, Pose2(0.0, 0.0, 0.0), priorNoise));
graph.add(PriorFactorPose2(2, Pose2(2.0, 0.0, 0.0), priorNoise));
graph.add(PriorFactorPose2(3, Pose2(4.0, 0.0, 0.0), priorNoise));
%% print
graph.print(sprintf('\nFactor graph:\n'));
%% Initialize to noisy points
import gtsam.*
initialEstimate = pose2SLAM.Values;
initialEstimate.insertPose(1, Pose2(0.5, 0.0, 0.2));
initialEstimate.insertPose(2, Pose2(2.3, 0.1,-0.2));
initialEstimate.insertPose(3, Pose2(4.1, 0.1, 0.1));
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, 0.1));
initialEstimate.print(sprintf('\nInitial estimate:\n '));
%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
import gtsam.*
result = graph.optimize(initialEstimate,1);
optimizer = LevenbergMarquardtOptimizer(graph, initialEstimate);
result = optimizer.optimizeSafely();
result.print(sprintf('\nFinal result:\n '));
%% Plot Covariance Ellipses
%% Plot trajectory and covariance ellipses
import gtsam.*
cla;
X=result.poses();
plot(X(:,1),X(:,2),'k*-'); hold on
marginals = graph.marginals(result);
P={};
for i=1:result.size()
pose_i = result.pose(i);
P{i}=marginals.marginalCovariance(i);
plotPose2(pose_i,'g',P{i})
end
hold on;
plot2DTrajectory(result, Marginals(graph, result));
axis([-0.6 4.8 -1 1])
axis equal
view(2)

34
matlab/examples/OdometryExample.m
View File

@ -16,48 +16,44 @@
% - The robot is on a grid, moving 2 meters each step
%% Create the graph (defined in pose2SLAM.h, derived from NonlinearFactorGraph)
graph = pose2SLAM.Graph;
graph = gtsam.NonlinearFactorGraph;
%% Add a Gaussian prior on pose x_1
import gtsam.*
priorMean = Pose2(0.0, 0.0, 0.0); % prior mean is at origin
priorNoise = noiseModel.Diagonal.Sigmas([0.3; 0.3; 0.1]); % 30cm std on x,y, 0.1 rad on theta
graph.addPosePrior(1, priorMean, priorNoise); % add directly to graph
graph.add(PriorFactorPose2(1, priorMean, priorNoise)); % add directly to graph
%% Add two odometry factors
import gtsam.*
odometry = Pose2(2.0, 0.0, 0.0); % create a measurement for both factors (the same in this case)
odometryNoise = noiseModel.Diagonal.Sigmas([0.2; 0.2; 0.1]); % 20cm std on x,y, 0.1 rad on theta
graph.addRelativePose(1, 2, odometry, odometryNoise);
graph.addRelativePose(2, 3, odometry, odometryNoise);
graph.add(BetweenFactorPose2(1, 2, odometry, odometryNoise));
graph.add(BetweenFactorPose2(2, 3, odometry, odometryNoise));
%% print
graph.print(sprintf('\nFactor graph:\n'));
%% Initialize to noisy points
import gtsam.*
initialEstimate = pose2SLAM.Values;
initialEstimate.insertPose(1, Pose2(0.5, 0.0, 0.2));
initialEstimate.insertPose(2, Pose2(2.3, 0.1,-0.2));
initialEstimate.insertPose(3, Pose2(4.1, 0.1, 0.1));
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, 0.1));
initialEstimate.print(sprintf('\nInitial estimate:\n '));
%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
result = graph.optimize(initialEstimate,1);
optimizer = LevenbergMarquardtOptimizer(graph, initialEstimate);
result = optimizer.optimizeSafely();
result.print(sprintf('\nFinal result:\n '));
%% Plot Covariance Ellipses
%% Plot trajectory and covariance ellipses
import gtsam.*
cla;
X=result.poses();
plot(X(:,1),X(:,2),'k*-'); hold on
marginals = graph.marginals(result);
P={};
for i=1:result.size()
pose_i = result.pose(i);
P{i}=marginals.marginalCovariance(i);
plotPose2(pose_i,'g',P{i})
end
hold on;
plot2DTrajectory(result, Marginals(graph, result));
axis([-0.6 4.8 -1 1])
axis equal
view(2)