81 lines
2.5 KiB
Matlab
81 lines
2.5 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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% @author Vadim Indelman
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% @author Can Erdogan
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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import gtsam.*
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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 = NonlinearFactorGraph;
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%% Add prior
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% gaussian for prior
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priorNoise = noiseModel.Diagonal.Sigmas([0.3; 0.3; 0.1]);
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priorMean = Pose2(0.0, 0.0, 0.0); % prior at origin
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graph.add(PriorFactorPose2(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 = noiseModel.Diagonal.Sigmas([0.2; 0.2; 0.1]);
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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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graph.add(BetweenFactorPose2(1, 2, odometry, odometryNoise));
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graph.add(BetweenFactorPose2(2, 3, odometry, odometryNoise));
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%% Add measurements
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% general noisemodel for measurements
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measurementNoise = noiseModel.Diagonal.Sigmas([0.1; 0.2]);
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% print
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graph.print('full graph');
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%% Initialize to noisy points
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initialEstimate = Values;
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initialEstimate.insert(1, Pose2(0.5, 0.0, 0.2));
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initialEstimate.insert(2, Pose2(2.3, 0.1,-0.2));
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initialEstimate.insert(3, Pose2(4.1, 0.1, 0.1));
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initialEstimate.print('initial estimate');
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%% set up solver, choose ordering and optimize
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params = DoglegParams;
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params.setAbsoluteErrorTol(1e-15);
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params.setRelativeErrorTol(1e-15);
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params.setVerbosity('ERROR');
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params.setVerbosityDL('VERBOSE');
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params.setOrdering(graph.orderingCOLAMD());
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optimizer = DoglegOptimizer(graph, initialEstimate, params);
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result = optimizer.optimizeSafely();
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result.print('final result');
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%% Get the corresponding dense matrix
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ord = graph.orderingCOLAMD();
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gfg = graph.linearize(result);
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denseAb = gfg.augmentedJacobian;
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%% Get sparse matrix A and RHS b
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IJS = gfg.sparseJacobian_();
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Ab=sparse(IJS(1,:),IJS(2,:),IJS(3,:));
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A = Ab(:,1:end-1);
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b = full(Ab(:,end));
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clf
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spy(A);
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title('Non-zero entries in measurement Jacobian');
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