81 lines
2.9 KiB
Matlab
81 lines
2.9 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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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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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 bearing and range information for measurements
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% - We have full odometry for measurements
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% - The robot and landmarks are on a grid, moving 2 meters each step
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% - Landmarks are 2 meters away from the robot trajectory
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%% Create keys for variables
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i1 = symbol('x',1); i2 = symbol('x',2); i3 = symbol('x',3);
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j1 = symbol('l',1); j2 = symbol('l',2);
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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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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(i1, priorMean, priorNoise));
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%% Add odometry
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odometry = Pose2(2.0, 0.0, 0.0);
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odometryNoise = noiseModel.Diagonal.Sigmas([0.2; 0.2; 0.1]);
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graph.add(BetweenFactorPose2(i1, i2, odometry, odometryNoise));
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graph.add(BetweenFactorPose2(i2, i3, odometry, odometryNoise));
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%% Add bearing/range measurement factors
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degrees = pi/180;
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brNoise = noiseModel.Diagonal.Sigmas([0.1; 0.2]);
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graph.add(BearingRangeFactor2D(i1, j1, Rot2(45*degrees), sqrt(8), brNoise));
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graph.add(BearingRangeFactor2D(i2, j1, Rot2(90*degrees), 2, brNoise));
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graph.add(BearingRangeFactor2D(i3, j2, Rot2(90*degrees), 2, brNoise));
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% print
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graph.print(sprintf('\nFull graph:\n'));
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%% Initialize to noisy points
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initialEstimate = Values;
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initialEstimate.insert(i1, Pose2(0.5, 0.0, 0.2));
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initialEstimate.insert(i2, Pose2(2.3, 0.1,-0.2));
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initialEstimate.insert(i3, Pose2(4.1, 0.1, 0.1));
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initialEstimate.insert(j1, Point2(1.8, 2.1));
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initialEstimate.insert(j2, Point2(4.1, 1.8));
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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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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 Covariance Ellipses
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cla;hold on
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marginals = Marginals(graph, result);
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plot2DTrajectory(result, [], marginals);
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plot2DPoints(result, 'b', marginals);
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plot([result.atPose2(i1).x; result.atPoint2(j1).x],[result.atPose2(i1).y; result.atPoint2(j1).y], 'c-');
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plot([result.atPose2(i2).x; result.atPoint2(j1).x],[result.atPose2(i2).y; result.atPoint2(j1).y], 'c-');
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plot([result.atPose2(i3).x; result.atPoint2(j2).x],[result.atPose2(i3).y; result.atPoint2(j2).y], 'c-');
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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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