69 lines
2.3 KiB
Matlab
69 lines
2.3 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 Example of a simple 2D localization example
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% @author Frank Dellaert
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%% Assumptions
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% - Robot poses are facing along the X axis (horizontal, to the right in 2D)
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% - The robot moves 2 meters each step
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% - The robot is on a grid, moving 2 meters each step
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%% Create the graph (defined in pose2SLAM.h, derived from NonlinearFactorGraph)
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graph = pose2SLAMGraph;
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%% Add a Gaussian prior on pose x_1
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priorMean = gtsamPose2(0.0, 0.0, 0.0); % prior mean is at origin
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priorNoise = gtsamSharedNoiseModel_Sigmas([0.3; 0.3; 0.1]); % 30cm std on x,y, 0.1 rad on theta
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graph.addPrior(1, priorMean, priorNoise); % add directly to graph
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%% Add two odometry factors
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odometry = gtsamPose2(2.0, 0.0, 0.0); % create a measurement for both factors (the same in this case)
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odometryNoise = gtsamSharedNoiseModel_Sigmas([0.2; 0.2; 0.1]); % 20cm std on x,y, 0.1 rad on theta
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graph.addOdometry(1, 2, odometry, odometryNoise);
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graph.addOdometry(2, 3, odometry, odometryNoise);
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%% print
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graph.print(sprintf('\nFactor graph:\n'));
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%% Initialize to noisy points
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initialEstimate = pose2SLAMValues;
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initialEstimate.insertPose(1, gtsamPose2(0.5, 0.0, 0.2));
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initialEstimate.insertPose(2, gtsamPose2(2.3, 0.1,-0.2));
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initialEstimate.insertPose(3, gtsamPose2(4.1, 0.1, 0.1));
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initialEstimate.print(sprintf('\nInitial estimate:\n '));
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%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
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result = graph.optimize(initialEstimate);
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result.print(sprintf('\nFinal result:\n '));
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%% Query the marginals
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marginals = graph.marginals(result);
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P{1}=marginals.marginalCovariance(1);
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P{2}=marginals.marginalCovariance(2);
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P{3}=marginals.marginalCovariance(3);
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%% Plot Trajectory
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figure(1)
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clf
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X=[];Y=[];
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for i=1:3
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pose_i = result.pose(i);
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X=[X;pose_i.x];
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Y=[Y;pose_i.y];
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end
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plot(X,Y,'b*-');
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%% Plot Covariance Ellipses
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hold on
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for i=1:3
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pose_i = result.pose(i);
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covarianceEllipse([pose_i.x;pose_i.y],P{i},'g')
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end
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