44 lines
1.7 KiB
Matlab
44 lines
1.7 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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import gtsam.*
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%% Create the graph (defined in pose2SLAM.h, derived from NonlinearFactorGraph)
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graph = NonlinearFactorGraph;
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%% Add a Gaussian prior on pose x_1
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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([0.3; 0.3; 0.1]); % 30cm std on x,y, 0.1 rad on theta
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graph.add(PriorFactorPose2(1, priorMean, priorNoise)); % add directly to graph
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%% Add two odometry factors
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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.add(BetweenFactorPose2(1, 2, odometry, odometryNoise));
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graph.add(BetweenFactorPose2(2, 3, odometry, odometryNoise));
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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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%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
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optimizer = LevenbergMarquardtOptimizer(graph, initialEstimate);
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result = optimizer.optimizeSafely();
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marginals = Marginals(graph, result);
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marginals.marginalCovariance(1);
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%% Check first pose equality
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pose_1 = result.at(1);
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CHECK('pose_1.equals(Pose2,1e-4)',pose_1.equals(Pose2,1e-4));
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