67 lines
2.2 KiB
Matlab
67 lines
2.2 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 = pose2SLAM.Graph;
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%% Add two odometry factors
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import gtsam.*
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odometry = Pose2(2.0, 0.0, 0.0); % create a measurement for both factors (the same in this case)
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odometryNoise = noiseModel.Diagonal.Sigmas([0.2; 0.2; 0.1]); % 20cm std on x,y, 0.1 rad on theta
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graph.addRelativePose(1, 2, odometry, odometryNoise);
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graph.addRelativePose(2, 3, odometry, odometryNoise);
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%% Add three "GPS" measurements
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import gtsam.*
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% We use Pose2 Priors here with high variance on theta
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priorNoise = noiseModel.Diagonal.Sigmas([0.1; 0.1; 10]);
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graph.addPosePrior(1, Pose2(0.0, 0.0, 0.0), priorNoise);
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graph.addPosePrior(2, Pose2(2.0, 0.0, 0.0), priorNoise);
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graph.addPosePrior(3, Pose2(4.0, 0.0, 0.0), priorNoise);
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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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import gtsam.*
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initialEstimate = pose2SLAM.Values;
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initialEstimate.insertPose(1, Pose2(0.5, 0.0, 0.2));
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initialEstimate.insertPose(2, Pose2(2.3, 0.1,-0.2));
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initialEstimate.insertPose(3, Pose2(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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import gtsam.*
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result = graph.optimize(initialEstimate,1);
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result.print(sprintf('\nFinal result:\n '));
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%% Plot Covariance Ellipses
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import gtsam.*
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cla;
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X=result.poses();
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plot(X(:,1),X(:,2),'k*-'); hold on
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marginals = graph.marginals(result);
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P={};
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for i=1:result.size()
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pose_i = result.pose(i);
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P{i}=marginals.marginalCovariance(i);
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plotPose2(pose_i,'g',P{i})
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end
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axis([-0.6 4.8 -1 1])
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axis equal
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view(2)
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