48 lines
1.6 KiB
Matlab
48 lines
1.6 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 Read graph from file and perform GraphSLAM
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% @author Frank Dellaert
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%% Find data file
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datafile = findExampleDataFile('w100-odom.graph');
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%% Initialize graph, initial estimate, and odometry noise
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import gtsam.*
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model = noiseModel.Diagonal.Sigmas([0.05; 0.05; 5*pi/180]);
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[graph,initial] = load2D(datafile, model);
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initial.print(sprintf('Initial estimate:\n'));
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%% Add a Gaussian prior on pose x_1
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import gtsam.*
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priorMean = Pose2(0.0, 0.0, 0.0); % prior mean is at origin
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priorNoise = noiseModel.Diagonal.Sigmas([0.01; 0.01; 0.01]);
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graph.add(PriorFactorPose2(0, priorMean, priorNoise)); % add directly to graph
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%% Plot Initial Estimate
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cla
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plot2DTrajectory(initial, 'g-*'); axis equal
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%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
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optimizer = LevenbergMarquardtOptimizer(graph, initial);
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result = optimizer.optimizeSafely;
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hold on; plot2DTrajectory(result, 'b-*');
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result.print(sprintf('\nFinal result:\n'));
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%% Plot Covariance Ellipses
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marginals = Marginals(graph, result);
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P={};
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for i=1:result.size()-1
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pose_i = result.at(i);
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P{i}=marginals.marginalCovariance(i);
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plotPose2(pose_i,'b',P{i})
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end
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view(2)
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axis([-15 10 -15 10]); axis equal;
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fprintf(1,'%.5f %.5f %.5f\n',P{99}) |