74 lines
2.6 KiB
Matlab
74 lines
2.6 KiB
Matlab
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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% GTSAM Copyright 2010, Georgia Tech Research Corporation,
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% Atlanta, Georgia 30332-0415
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% All Rights Reserved
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% Authors: Frank Dellaert, et al. (see THANKS for the full author list)
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%
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% See LICENSE for the license information
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%
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% @brief Simple robotics example using the pre-built planar SLAM domain
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% @author Alex Cunningham
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% @author Frank Dellaert
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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import gtsam.*
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%% Create the same factor graph as in PlanarSLAMExample
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i1 = symbol('x',1); i2 = symbol('x',2); i3 = symbol('x',3);
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graph = NonlinearFactorGraph;
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priorMean = Pose2(0.0, 0.0, 0.0); % prior at origin
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priorNoise = noiseModel.Diagonal.Sigmas([0.3; 0.3; 0.1]);
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graph.add(PriorFactorPose2(i1, priorMean, priorNoise)); % add directly to graph
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odometry = Pose2(2.0, 0.0, 0.0);
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odometryNoise = noiseModel.Diagonal.Sigmas([0.2; 0.2; 0.1]);
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graph.add(BetweenFactorPose2(i1, i2, odometry, odometryNoise));
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graph.add(BetweenFactorPose2(i2, i3, odometry, odometryNoise));
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%% Except, for measurements we offer a choice
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j1 = symbol('l',1); j2 = symbol('l',2);
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degrees = pi/180;
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brNoise = noiseModel.Diagonal.Sigmas([0.1; 0.2]);
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if 1
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graph.add(BearingRangeFactor2D(i1, j1, Rot2(45*degrees), sqrt(4+4), brNoise));
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graph.add(BearingRangeFactor2D(i2, j1, Rot2(90*degrees), 2, brNoise));
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else
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bearingModel = noiseModel.Diagonal.Sigmas(0.1);
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graph.add(BearingFactor2D(i1, j1, Rot2(45*degrees), bearingModel));
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graph.add(BearingFactor2D(i2, j1, Rot2(90*degrees), bearingModel));
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end
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graph.add(BearingRangeFactor2D(i3, j2, Rot2(90*degrees), 2, brNoise));
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%% Initialize MCMC sampler with ground truth
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sample = Values;
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sample.insert(i1, Pose2(0,0,0));
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sample.insert(i2, Pose2(2,0,0));
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sample.insert(i3, Pose2(4,0,0));
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sample.insert(j1, Point2(2,2));
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sample.insert(j2, Point2(4,2));
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%% Calculate and plot Covariance Ellipses
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cla;hold on
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marginals = Marginals(graph, sample);
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plot2DTrajectory(sample, [], marginals);
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plot2DPoints(sample, [], marginals);
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for j=1:2
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key = symbol('l',j);
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point{j} = sample.at(key);
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Q{j}=marginals.marginalCovariance(key);
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S{j}=chol(Q{j}); % for sampling
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end
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plot([sample.at(i1).x; sample.at(j1).x],[sample.at(i1).y; sample.at(j1).y], 'c-');
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plot([sample.at(i2).x; sample.at(j1).x],[sample.at(i2).y; sample.at(j1).y], 'c-');
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plot([sample.at(i3).x; sample.at(j2).x],[sample.at(i3).y; sample.at(j2).y], 'c-');
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view(2); axis auto; axis equal
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%% Do Sampling on point 2
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N=1000;
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for s=1:N
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delta = S{2}*randn(2,1);
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proposedPoint = Point2(point{2}.x+delta(1),point{2}.y+delta(2));
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plotPoint2(proposedPoint,'k.')
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end |