73 lines
2.6 KiB
Matlab
73 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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% @author Chris Beall
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%% Assumptions
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% - All values are axis aligned
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% - Robot poses are facing along the X axis (horizontal, to the right in images)
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% - We have full odometry for measurements
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% - The robot is on a grid, moving 2 meters each step
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%% Create graph container and add factors to it
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graph = pose2SLAMGraph;
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%% Add prior
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% gaussian for prior
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priorMean = gtsamPose2(0.0, 0.0, 0.0); % prior at origin
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priorNoise = gtsamnoiseModelDiagonal.Sigmas([0.3; 0.3; 0.1]);
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graph.addPosePrior(1, priorMean, priorNoise); % add directly to graph
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%% Add odometry
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% general noisemodel for odometry
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odometryNoise = gtsamnoiseModelDiagonal.Sigmas([0.2; 0.2; 0.1]);
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graph.addRelativePose(1, 2, gtsamPose2(2.0, 0.0, 0.0 ), odometryNoise);
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graph.addRelativePose(2, 3, gtsamPose2(2.0, 0.0, pi/2), odometryNoise);
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graph.addRelativePose(3, 4, gtsamPose2(2.0, 0.0, pi/2), odometryNoise);
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graph.addRelativePose(4, 5, gtsamPose2(2.0, 0.0, pi/2), odometryNoise);
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%% Add pose constraint
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model = gtsamnoiseModelDiagonal.Sigmas([0.2; 0.2; 0.1]);
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graph.addRelativePose(5, 2, gtsamPose2(2.0, 0.0, pi/2), model);
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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, pi/2));
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initialEstimate.insertPose(4, gtsamPose2(4.0, 2.0, pi ));
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initialEstimate.insertPose(5, gtsamPose2(2.1, 2.1,-pi/2));
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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,1);
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result.print(sprintf('\nFinal result:\n'));
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%% Plot Covariance Ellipses
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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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plot([result.pose(5).x;result.pose(2).x],[result.pose(5).y;result.pose(2).y],'r-');
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marginals = graph.marginals(result);
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for i=1:result.size()
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pose_i = result.pose(i);
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P = marginals.marginalCovariance(i)
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plotPose2(pose_i,'g',P);
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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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