79 lines
2.8 KiB
Matlab
79 lines
2.8 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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%% 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 = planarSLAMGraph;
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priorMean = gtsamPose2(0.0, 0.0, 0.0); % prior at origin
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priorNoise = gtsamSharedNoiseModel_Sigmas([0.3; 0.3; 0.1]);
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graph.addPrior(i1, priorMean, priorNoise); % add directly to graph
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odometry = gtsamPose2(2.0, 0.0, 0.0);
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odometryNoise = gtsamSharedNoiseModel_Sigmas([0.2; 0.2; 0.1]);
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graph.addOdometry(i1, i2, odometry, odometryNoise);
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graph.addOdometry(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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noiseModel = gtsamSharedNoiseModel_Sigmas([0.1; 0.2]);
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if 1
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graph.addBearingRange(i1, j1, gtsamRot2(45*degrees), sqrt(4+4), noiseModel);
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graph.addBearingRange(i2, j1, gtsamRot2(90*degrees), 2, noiseModel);
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else
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bearingModel = gtsamSharedNoiseModel_Sigmas(0.1);
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graph.addBearing(i1, j1, gtsamRot2(45*degrees), bearingModel);
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graph.addBearing(i2, j1, gtsamRot2(90*degrees), bearingModel);
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end
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graph.addBearingRange(i3, j2, gtsamRot2(90*degrees), 2, noiseModel);
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%% Initialize MCMC sampler with ground truth
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sample = planarSLAMValues;
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sample.insertPose(i1, gtsamPose2(0,0,0));
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sample.insertPose(i2, gtsamPose2(2,0,0));
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sample.insertPose(i3, gtsamPose2(4,0,0));
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sample.insertPoint(j1, gtsamPoint2(2,2));
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sample.insertPoint(j2, gtsamPoint2(4,2));
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%% Calculate and plot Covariance Ellipses
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figure(1);clf;hold on
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marginals = graph.marginals(sample);
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for i=1:3
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key = symbol('x',i);
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pose{i} = sample.pose(key);
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P{i}=marginals.marginalCovariance(key);
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if i>1
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plot([pose{i-1}.x;pose{i}.x],[pose{i-1}.y;pose{i}.y],'r-');
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end
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end
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for i=1:3
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plotPose2(pose{i},'g',P{i})
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end
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for j=1:2
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key = symbol('l',j);
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point{j} = sample.point(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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plotPoint2(point{j},'b',Q{j})
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end
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plot([pose{1}.x;point{1}.x],[pose{1}.y;point{1}.y],'c-');
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plot([pose{2}.x;point{1}.x],[pose{2}.y;point{1}.y],'c-');
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plot([pose{3}.x;point{2}.x],[pose{3}.y;point{2}.y],'c-');
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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 = gtsamPoint2(point{2}.x+delta(1),point{2}.y+delta(2));
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plotPoint2(proposedPoint,'k.')
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end |