Updating matlab unit tests
Richard Roberts
parent
6bee17b603
commit
1724267c85
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@ -11,41 +11,46 @@
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%% Create the graph (defined in pose2SLAM.h, derived from NonlinearFactorGraph)
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graph = pose2SLAM.Graph;
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import gtsam.*
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graph = NonlinearFactorGraph;
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%% Add two odometry factors
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import gtsam.*
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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.addRelativePose(1, 2, odometry, odometryNoise);
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graph.addRelativePose(2, 3, odometry, odometryNoise);
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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 three "GPS" measurements
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% We use Pose2 Priors here with high variance on theta
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import gtsam.*
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groundTruth = pose2SLAM.Values;
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groundTruth.insertPose(1, Pose2(0.0, 0.0, 0.0));
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groundTruth.insertPose(2, Pose2(2.0, 0.0, 0.0));
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groundTruth.insertPose(3, Pose2(4.0, 0.0, 0.0));
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groundTruth = Values;
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groundTruth.insert(1, Pose2(0.0, 0.0, 0.0));
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groundTruth.insert(2, Pose2(2.0, 0.0, 0.0));
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groundTruth.insert(3, Pose2(4.0, 0.0, 0.0));
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model = noiseModel.Diagonal.Sigmas([0.1; 0.1; 10]);
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for i=1:3
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graph.addPosePrior(i, groundTruth.pose(i), model);
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graph.add(PriorFactorPose2(i, groundTruth.at(i), model));
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end
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%% Initialize to noisy points
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initialEstimate = pose2SLAM.Values;
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initialEstimate.insertPose(1, Pose2(0.5, 0.0, 0.2));
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initialEstimate.insertPose(2, Pose2(2.3, 0.1,-0.2));
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initialEstimate.insertPose(3, Pose2(4.1, 0.1, 0.1));
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import gtsam.*
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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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result = graph.optimize(initialEstimate,0);
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import gtsam.*
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optimizer = LevenbergMarquardtOptimizer(graph, initialEstimate);
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result = optimizer.optimizeSafely();
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%% Plot Covariance Ellipses
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marginals = graph.marginals(result);
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import gtsam.*
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marginals = Marginals(graph, result);
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P={};
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for i=1:result.size()
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pose_i = result.pose(i);
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CHECK('pose_i.equals(groundTruth.pose(i)',pose_i.equals(groundTruth.pose(i),1e-4));
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pose_i = result.at(i);
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CHECK('pose_i.equals(groundTruth.pose(i)',pose_i.equals(groundTruth.at(i),1e-4));
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P{i}=marginals.marginalCovariance(i);
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end
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@ -11,33 +11,35 @@
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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%% Create the graph (defined in pose2SLAM.h, derived from NonlinearFactorGraph)
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graph = pose2SLAM.Graph;
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import gtsam.*
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graph = NonlinearFactorGraph;
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%% Add a Gaussian prior on pose x_1
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import gtsam.*
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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.addPosePrior(1, priorMean, priorNoise); % add directly to graph
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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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import gtsam.*
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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.addRelativePose(1, 2, odometry, odometryNoise);
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graph.addRelativePose(2, 3, odometry, odometryNoise);
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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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import gtsam.*
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initialEstimate = pose2SLAM.Values;
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initialEstimate.insertPose(1, Pose2(0.5, 0.0, 0.2));
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initialEstimate.insertPose(2, Pose2(2.3, 0.1,-0.2));
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initialEstimate.insertPose(3, Pose2(4.1, 0.1, 0.1));
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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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result = graph.optimize(initialEstimate,0);
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marginals = graph.marginals(result);
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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.pose(1);
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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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@ -24,49 +24,52 @@ 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 = planarSLAM.Graph;
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import gtsam.*
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graph = NonlinearFactorGraph;
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%% Add prior
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import gtsam.*
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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.addPosePrior(i1, priorMean, priorNoise); % add directly to graph
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graph.add(PriorFactorPose2(i1, priorMean, priorNoise)); % add directly to graph
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%% Add odometry
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import gtsam.*
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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.addRelativePose(i1, i2, odometry, odometryNoise);
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graph.addRelativePose(i2, i3, odometry, odometryNoise);
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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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import gtsam.*
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degrees = pi/180;
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brNoise = noiseModel.Diagonal.Sigmas([0.1; 0.2]);
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graph.addBearingRange(i1, j1, Rot2(45*degrees), sqrt(4+4), brNoise);
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graph.addBearingRange(i2, j1, Rot2(90*degrees), 2, brNoise);
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graph.addBearingRange(i3, j2, Rot2(90*degrees), 2, brNoise);
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graph.add(BearingRangeFactor2D(i1, j1, Rot2(45*degrees), sqrt(4+4), 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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%% Initialize to noisy points
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import gtsam.*
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initialEstimate = planarSLAM.Values;
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initialEstimate.insertPose(i1, Pose2(0.5, 0.0, 0.2));
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initialEstimate.insertPose(i2, Pose2(2.3, 0.1,-0.2));
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initialEstimate.insertPose(i3, Pose2(4.1, 0.1, 0.1));
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initialEstimate.insertPoint(j1, Point2(1.8, 2.1));
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initialEstimate.insertPoint(j2, Point2(4.1, 1.8));
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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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%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
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result = graph.optimize(initialEstimate,0);
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marginals = graph.marginals(result);
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import gtsam.*
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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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%% Check first pose and point equality
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import gtsam.*
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pose_1 = result.pose(symbol('x',1));
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pose_1 = result.at(symbol('x',1));
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marginals.marginalCovariance(symbol('x',1));
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CHECK('pose_1.equals(Pose2,1e-4)',pose_1.equals(Pose2,1e-4));
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point_1 = result.point(symbol('l',1));
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point_1 = result.at(symbol('l',1));
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marginals.marginalCovariance(symbol('l',1));
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CHECK('point_1.equals(Point2(2,2),1e-4)',point_1.equals(Point2(2,2),1e-4));
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@ -19,50 +19,60 @@
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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 = pose2SLAM.Graph;
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import gtsam.*
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graph = NonlinearFactorGraph;
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%% Add prior
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import gtsam.*
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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.addPosePrior(1, priorMean, priorNoise); % add directly to graph
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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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import gtsam.*
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odometryNoise = noiseModel.Diagonal.Sigmas([0.2; 0.2; 0.1]);
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graph.addRelativePose(1, 2, Pose2(2.0, 0.0, 0.0 ), odometryNoise);
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graph.addRelativePose(2, 3, Pose2(2.0, 0.0, pi/2), odometryNoise);
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graph.addRelativePose(3, 4, Pose2(2.0, 0.0, pi/2), odometryNoise);
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graph.addRelativePose(4, 5, Pose2(2.0, 0.0, pi/2), odometryNoise);
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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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import gtsam.*
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model = noiseModel.Diagonal.Sigmas([0.2; 0.2; 0.1]);
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graph.addRelativePose(5, 2, Pose2(2.0, 0.0, pi/2), model);
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graph.add(BetweenFactorPose2(5, 2, Pose2(2.0, 0.0, pi/2), model));
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%% Initialize to noisy points
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import gtsam.*
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initialEstimate = pose2SLAM.Values;
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initialEstimate.insertPose(1, Pose2(0.5, 0.0, 0.2 ));
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initialEstimate.insertPose(2, Pose2(2.3, 0.1,-0.2 ));
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initialEstimate.insertPose(3, Pose2(4.1, 0.1, pi/2));
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initialEstimate.insertPose(4, Pose2(4.0, 2.0, pi ));
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initialEstimate.insertPose(5, Pose2(2.1, 2.1,-pi/2));
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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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%% Optimize using Levenberg-Marquardt optimization with an ordering from colamd
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result = graph.optimize(initialEstimate,0);
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resultSPCG = graph.optimizeSPCG(initialEstimate,0);
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import gtsam.*
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optimizer = LevenbergMarquardtOptimizer(graph, initialEstimate);
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result = optimizer.optimizeSafely();
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%% Optimize using SPCG
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%import gtsam.*
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%params = LevenbergMarquardtParams;
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%params.setLinearSolverType('CONJUGATE_GRADIENT');
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%optimizerSPCG = LevenbergMarquardtOptimizer(graph, initialEstimate, params);
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%resultSPCG = optimizerSPCG.optimize();
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%% Plot Covariance Ellipses
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marginals = graph.marginals(result);
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import gtsam.*
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marginals = Marginals(graph, result);
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P = marginals.marginalCovariance(1);
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pose_1 = result.pose(1);
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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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poseSPCG_1 = resultSPCG.pose(1);
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CHECK('poseSPCG_1.equals(Pose2,1e-4)',poseSPCG_1.equals(Pose2,1e-4));
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%poseSPCG_1 = resultSPCG.at(1);
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%CHECK('poseSPCG_1.equals(Pose2,1e-4)',poseSPCG_1.equals(Pose2,1e-4));
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@ -17,31 +17,32 @@ p1 = hexagon.pose(1);
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%% create a Pose graph with one equality constraint and one measurement
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import gtsam.*
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fg = pose3SLAM.Graph;
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fg.addPoseConstraint(0, p0);
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fg = NonlinearFactorGraph;
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fg.add(NonlinearEqualityPose3(0, p0));
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delta = p0.between(p1);
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covariance = noiseModel.Diagonal.Sigmas([0.05; 0.05; 0.05; 5*pi/180; 5*pi/180; 5*pi/180]);
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fg.addRelativePose(0,1, delta, covariance);
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fg.addRelativePose(1,2, delta, covariance);
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fg.addRelativePose(2,3, delta, covariance);
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fg.addRelativePose(3,4, delta, covariance);
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fg.addRelativePose(4,5, delta, covariance);
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fg.addRelativePose(5,0, delta, covariance);
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fg.add(BetweenFactorPose3(0,1, delta, covariance));
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fg.add(BetweenFactorPose3(1,2, delta, covariance));
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fg.add(BetweenFactorPose3(2,3, delta, covariance));
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fg.add(BetweenFactorPose3(3,4, delta, covariance));
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fg.add(BetweenFactorPose3(4,5, delta, covariance));
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fg.add(BetweenFactorPose3(5,0, delta, covariance));
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%% Create initial config
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initial = pose3SLAM.Values;
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initial = Values;
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s = 0.10;
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initial.insertPose(0, p0);
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initial.insertPose(1, hexagon.pose(1).retract(s*randn(6,1)));
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initial.insertPose(2, hexagon.pose(2).retract(s*randn(6,1)));
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initial.insertPose(3, hexagon.pose(3).retract(s*randn(6,1)));
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initial.insertPose(4, hexagon.pose(4).retract(s*randn(6,1)));
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initial.insertPose(5, hexagon.pose(5).retract(s*randn(6,1)));
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initial.insert(0, p0);
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initial.insert(1, hexagon.pose(1).retract(s*randn(6,1)));
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initial.insert(2, hexagon.pose(2).retract(s*randn(6,1)));
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initial.insert(3, hexagon.pose(3).retract(s*randn(6,1)));
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initial.insert(4, hexagon.pose(4).retract(s*randn(6,1)));
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initial.insert(5, hexagon.pose(5).retract(s*randn(6,1)));
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%% optimize
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result = fg.optimize(initial,0);
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optimizer = LevenbergMarquardtOptimizer(fg, initial);
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result = optimizer.optimizeSafely;
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pose_1 = result.pose(1);
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pose_1 = result.at(1);
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CHECK('pose_1.equals(Pose3,1e-4)',pose_1.equals(p1,1e-4));
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@ -10,17 +10,19 @@
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% @author Duy-Nguyen Ta
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%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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import gtsam.*
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options.triangle = false;
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options.nrCameras = 10;
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options.showImages = false;
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[data,truth] = VisualISAMGenerateData(options);
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[data,truth] = support.VisualISAMGenerateData(options);
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measurementNoiseSigma = 1.0;
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pointNoiseSigma = 0.1;
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poseNoiseSigmas = [0.001 0.001 0.001 0.1 0.1 0.1]';
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graph = visualSLAM.Graph;
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graph = NonlinearFactorGraph;
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%% Add factors for all measurements
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import gtsam.*
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@ -28,47 +30,43 @@ measurementNoise = noiseModel.Isotropic.Sigma(2,measurementNoiseSigma);
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for i=1:length(data.Z)
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for k=1:length(data.Z{i})
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j = data.J{i}{k};
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graph.addMeasurement(data.Z{i}{k}, measurementNoise, symbol('x',i), symbol('p',j), data.K);
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graph.add(GenericProjectionFactorCal3_S2(data.Z{i}{k}, measurementNoise, symbol('x',i), symbol('p',j), data.K));
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end
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end
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posePriorNoise = noiseModel.Diagonal.Sigmas(poseNoiseSigmas);
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graph.addPosePrior(symbol('x',1), truth.cameras{1}.pose, posePriorNoise);
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graph.add(PriorFactorPose3(symbol('x',1), truth.cameras{1}.pose, posePriorNoise));
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pointPriorNoise = noiseModel.Isotropic.Sigma(3,pointNoiseSigma);
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graph.addPointPrior(symbol('p',1), truth.points{1}, pointPriorNoise);
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graph.add(PriorFactorPose3(symbol('p',1), truth.points{1}, pointPriorNoise));
|
||||
|
||||
%% Initial estimate
|
||||
initialEstimate = visualSLAM.Values;
|
||||
initialEstimate = Values;
|
||||
for i=1:size(truth.cameras,2)
|
||||
pose_i = truth.cameras{i}.pose;
|
||||
initialEstimate.insertPose(symbol('x',i), pose_i);
|
||||
initialEstimate.insert(symbol('x',i), pose_i);
|
||||
end
|
||||
for j=1:size(truth.points,2)
|
||||
point_j = truth.points{j};
|
||||
initialEstimate.insertPoint(symbol('p',j), point_j);
|
||||
initialEstimate.insert(symbol('p',j), point_j);
|
||||
end
|
||||
|
||||
%% Optimization
|
||||
import gtsam.*
|
||||
parameters = LevenbergMarquardtParams;
|
||||
optimizer = graph.optimizer(initialEstimate, parameters);
|
||||
optimizer = LevenbergMarquardtOptimizer(graph, initialEstimate);
|
||||
for i=1:5
|
||||
optimizer.iterate();
|
||||
end
|
||||
result = optimizer.values();
|
||||
|
||||
%% Marginalization
|
||||
marginals = graph.marginals(result);
|
||||
marginals = Marginals(graph, result);
|
||||
marginals.marginalCovariance(symbol('p',1));
|
||||
marginals.marginalCovariance(symbol('x',1));
|
||||
|
||||
%% Check optimized results, should be equal to ground truth
|
||||
for i=1:size(truth.cameras,2)
|
||||
pose_i = result.pose(symbol('x',i));
|
||||
CHECK('pose_i.equals(truth.cameras{i}.pose,1e-5)',pose_i.equals(truth.cameras{i}.pose,1e-5))
|
||||
end
|
||||
|
||||
for j=1:size(truth.points,2)
|
||||
point_j = result.point(symbol('p',j));
|
||||
CHECK('point_j.equals(truth.points{j},1e-5)',point_j.equals(truth.points{j},1e-5))
|
||||
keys = truth.keys;
|
||||
for i=0:keys.size-1
|
||||
truth_i = truth.at(keys.at(i));
|
||||
result_i = result.at(keys.at(i));
|
||||
CHECK('result_i.equals(truth_i,1e-5)',result_i.equals(truth_i,1e-5))
|
||||
end
|
||||
|
|
|
|||
Loading…
Reference in New Issue