65 lines
2.3 KiB
Matlab
65 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 Simple 2D robotics example using the SimpleSPCGSolver
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% @author Yong-Dian Jian
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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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priorMean = Pose2(0.0, 0.0, 0.0); % prior at origin
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priorNoise = noiseModel.Diagonal.Sigmas([0.3; 0.3; 0.1]);
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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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graph.add(BetweenFactorPose2(1, 2, Pose2(2.0, 0.0, 0.0 ), odometryNoise));
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graph.add(BetweenFactorPose2(2, 3, Pose2(2.0, 0.0, pi/2), odometryNoise));
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graph.add(BetweenFactorPose2(3, 4, Pose2(2.0, 0.0, pi/2), odometryNoise));
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graph.add(BetweenFactorPose2(4, 5, Pose2(2.0, 0.0, pi/2), odometryNoise));
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%% Add pose constraint
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model = noiseModel.Diagonal.Sigmas([0.2; 0.2; 0.1]);
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graph.add(BetweenFactorPose2(5, 2, Pose2(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 = 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, pi/2));
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initialEstimate.insert(4, Pose2(4.0, 2.0, pi ));
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initialEstimate.insert(5, Pose2(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 SubgraphSolver
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params = gtsam.LevenbergMarquardtParams;
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subgraphParams = gtsam.SubgraphSolverParameters;
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params.setLinearSolverType('CONJUGATE_GRADIENT');
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params.setIterativeParams(subgraphParams);
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optimizer = gtsam.LevenbergMarquardtOptimizer(graph, initialEstimate);
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result = optimizer.optimize();
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result.print(sprintf('\nFinal result:\n'));
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