69 lines
2.7 KiB
Matlab
69 lines
2.7 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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% Copied Original file. Modified by David Jensen to use Pose3 instead of
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% Pose2. Everything else is the same.
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import gtsam.*
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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 = NonlinearFactorGraph;
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%% Add a Gaussian prior on pose x_1
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priorMean = Pose3();%Pose3.Expmap([0.0; 0.0; 0.0; 0.0; 0.0; 0.0]); % prior mean is at origin
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priorNoise = noiseModel.Diagonal.Sigmas([0.1; 0.1; 0.1; 0.3; 0.3; 0.3]); % 30cm std on x,y,z 0.1 rad on roll,pitch,yaw
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graph.add(PriorFactorPose3(1, priorMean, priorNoise)); % add directly to graph
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%% Add two odometry factors
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odometry = Pose3.Expmap([0.0; 0.0; 0.0; 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([0.1; 0.1; 0.1; 0.2; 0.2; 0.2]); % 20cm std on x,y,z 0.1 rad on roll,pitch,yaw
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graph.add(BetweenFactorPose3(1, 2, odometry, odometryNoise));
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graph.add(BetweenFactorPose3(2, 3, odometry, odometryNoise));
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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, Pose3.Expmap([0.2; 0.1; 0.1; 0.5; 0.0; 0.0]));
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%initialEstimate.insert(2, Pose3.Expmap([-0.2; 0.1; -0.1; 2.3; 0.1; 0.1]));
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%initialEstimate.insert(3, Pose3.Expmap([0.1; -0.1; 0.1; 4.1; 0.1; -0.1]));
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%initialEstimate.print(sprintf('\nInitial estimate:\n '));
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for i=1:3
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deltaPosition = 0.5*rand(3,1) + [1;0;0]; % create random vector with mean = [1 0 0] and sigma = 0.5
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deltaRotation = 0.1*rand(3,1) + [0;0;0]; % create random rotation with mean [0 0 0] and sigma = 0.1 (rad)
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deltaPose = Pose3.Expmap([deltaRotation; deltaPosition]);
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currentPose = currentPose.compose(deltaPose);
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initialEstimate.insert(i, currentPose);
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end
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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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result.print(sprintf('\nFinal result:\n '));
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%% Plot trajectory and covariance ellipses
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cla;
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hold on;
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plot3DTrajectory(result, [], Marginals(graph, result));
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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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