Fixing usage of globals in matlab examples/tests. Currently, tests fail due to handling of noisemodel

matlab/VisualISAMGenerateData.m

@@ -29,7 +29,7 @@ data.K = truth.K;
 for i=1:options.nrCameras
     theta = (i-1)*2*pi/options.nrCameras;
     t = gtsamPoint3([r*cos(theta), r*sin(theta), height]');
-    truth.cameras{i} = gtsamSimpleCamera_lookat(t, gtsamPoint3, gtsamPoint3([0,0,1]'), truth.K);
+    truth.cameras{i} = gtsamSimpleCamera.Lookat(t, gtsamPoint3, gtsamPoint3([0,0,1]'), truth.K);
     % Create measurements
     for j=1:nrPoints
         % All landmarks seen in every frame

matlab/VisualISAMInitialize.m

@@ -6,10 +6,10 @@ function [noiseModels,isam,result] = VisualISAMInitialize(data,truth,options)
 isam = visualSLAMISAM(options.reorderInterval);
 
 %% Set Noise parameters
-noiseModels.pose = gtsamnoiseModelDiagonal_Sigmas([0.001 0.001 0.001 0.1 0.1 0.1]');
+noiseModels.pose = gtsamnoiseModelDiagonal.Sigmas([0.001 0.001 0.001 0.1 0.1 0.1]');
-noiseModels.odometry = gtsamnoiseModelDiagonal_Sigmas([0.001 0.001 0.001 0.1 0.1 0.1]');
+noiseModels.odometry = gtsamnoiseModelDiagonal.Sigmas([0.001 0.001 0.001 0.1 0.1 0.1]');
-noiseModels.point = gtsamnoiseModelIsotropic_Sigma(3, 0.1);
+noiseModels.point = gtsamnoiseModelIsotropic.Sigma(3, 0.1);
-noiseModels.measurement = gtsamnoiseModelIsotropic_Sigma(2, 1.0);
+noiseModels.measurement = gtsamnoiseModelIsotropic.Sigma(2, 1.0);
 
 %% Add constraints/priors
 % TODO: should not be from ground truth!

matlab/examples/LocalizationExample.m

@@ -20,13 +20,13 @@ graph = pose2SLAMGraph;
 
 %% Add two odometry factors
 odometry = gtsamPose2(2.0, 0.0, 0.0); % create a measurement for both factors (the same in this case)
-odometryNoise = gtsamnoiseModelDiagonal_Sigmas([0.2; 0.2; 0.1]); % 20cm std on x,y, 0.1 rad on theta
+odometryNoise = gtsamnoiseModelDiagonal.Sigmas([0.2; 0.2; 0.1]); % 20cm std on x,y, 0.1 rad on theta
 graph.addRelativePose(1, 2, odometry, odometryNoise);
 graph.addRelativePose(2, 3, odometry, odometryNoise);
 
 %% Add three "GPS" measurements
 % We use Pose2 Priors here with high variance on theta
-noiseModel = gtsamnoiseModelDiagonal_Sigmas([0.1; 0.1; 10]);
+noiseModel = gtsamnoiseModelDiagonal.Sigmas([0.1; 0.1; 10]);
 graph.addPosePrior(1, gtsamPose2(0.0, 0.0, 0.0), noiseModel);
 graph.addPosePrior(2, gtsamPose2(2.0, 0.0, 0.0), noiseModel);
 graph.addPosePrior(3, gtsamPose2(4.0, 0.0, 0.0), noiseModel);

matlab/examples/OdometryExample.m

@@ -20,12 +20,12 @@ graph = pose2SLAMGraph;
 
 %% Add a Gaussian prior on pose x_1
 priorMean = gtsamPose2(0.0, 0.0, 0.0); % prior mean is at origin
-priorNoise = gtsamnoiseModelDiagonal_Sigmas([0.3; 0.3; 0.1]); % 30cm std on x,y, 0.1 rad on theta
+priorNoise = gtsamnoiseModelDiagonal.Sigmas([0.3; 0.3; 0.1]); % 30cm std on x,y, 0.1 rad on theta
 graph.addPosePrior(1, priorMean, priorNoise); % add directly to graph
 
 %% Add two odometry factors
 odometry = gtsamPose2(2.0, 0.0, 0.0); % create a measurement for both factors (the same in this case)
-odometryNoise = gtsamnoiseModelDiagonal_Sigmas([0.2; 0.2; 0.1]); % 20cm std on x,y, 0.1 rad on theta
+odometryNoise = gtsamnoiseModelDiagonal.Sigmas([0.2; 0.2; 0.1]); % 20cm std on x,y, 0.1 rad on theta
 graph.addRelativePose(1, 2, odometry, odometryNoise);
 graph.addRelativePose(2, 3, odometry, odometryNoise);
 

matlab/examples/PlanarSLAMExample.m

@@ -28,18 +28,18 @@ graph = planarSLAMGraph;
 
 %% Add prior
 priorMean = gtsamPose2(0.0, 0.0, 0.0); % prior at origin
-priorNoise = gtsamnoiseModelDiagonal_Sigmas([0.3; 0.3; 0.1]);
+priorNoise = gtsamnoiseModelDiagonal.Sigmas([0.3; 0.3; 0.1]);
 graph.addPosePrior(i1, priorMean, priorNoise); % add directly to graph
 
 %% Add odometry
 odometry = gtsamPose2(2.0, 0.0, 0.0);
-odometryNoise = gtsamnoiseModelDiagonal_Sigmas([0.2; 0.2; 0.1]);
+odometryNoise = gtsamnoiseModelDiagonal.Sigmas([0.2; 0.2; 0.1]);
 graph.addRelativePose(i1, i2, odometry, odometryNoise);
 graph.addRelativePose(i2, i3, odometry, odometryNoise);
 
 %% Add bearing/range measurement factors
 degrees = pi/180;
-noiseModel = gtsamnoiseModelDiagonal_Sigmas([0.1; 0.2]);
+noiseModel = gtsamnoiseModelDiagonal.Sigmas([0.1; 0.2]);
 graph.addBearingRange(i1, j1, gtsamRot2(45*degrees), sqrt(4+4), noiseModel);
 graph.addBearingRange(i2, j1, gtsamRot2(90*degrees), 2, noiseModel);
 graph.addBearingRange(i3, j2, gtsamRot2(90*degrees), 2, noiseModel);

matlab/examples/PlanarSLAMExample_sampling.m

@@ -15,22 +15,22 @@
 i1 = symbol('x',1); i2 = symbol('x',2); i3 = symbol('x',3);
 graph = planarSLAMGraph;
 priorMean = gtsamPose2(0.0, 0.0, 0.0); % prior at origin
-priorNoise = gtsamnoiseModelDiagonal_Sigmas([0.3; 0.3; 0.1]);
+priorNoise = gtsamnoiseModelDiagonal.Sigmas([0.3; 0.3; 0.1]);
 graph.addPosePrior(i1, priorMean, priorNoise); % add directly to graph
 odometry = gtsamPose2(2.0, 0.0, 0.0);
-odometryNoise = gtsamnoiseModelDiagonal_Sigmas([0.2; 0.2; 0.1]);
+odometryNoise = gtsamnoiseModelDiagonal.Sigmas([0.2; 0.2; 0.1]);
 graph.addRelativePose(i1, i2, odometry, odometryNoise);
 graph.addRelativePose(i2, i3, odometry, odometryNoise);
 
 %% Except, for measurements we offer a choice
 j1 = symbol('l',1); j2 = symbol('l',2);
 degrees = pi/180;
-noiseModel = gtsamnoiseModelDiagonal_Sigmas([0.1; 0.2]);
+noiseModel = gtsamnoiseModelDiagonal.Sigmas([0.1; 0.2]);
 if 1
     graph.addBearingRange(i1, j1, gtsamRot2(45*degrees), sqrt(4+4), noiseModel);
     graph.addBearingRange(i2, j1, gtsamRot2(90*degrees), 2, noiseModel);
 else
-    bearingModel = gtsamnoiseModelDiagonal_Sigmas(0.1);    
+    bearingModel = gtsamnoiseModelDiagonal.Sigmas(0.1);    
     graph.addBearing(i1, j1, gtsamRot2(45*degrees), bearingModel);
     graph.addBearing(i2, j1, gtsamRot2(90*degrees), bearingModel);
 end

matlab/examples/Pose2SLAMExample.m

@@ -24,19 +24,19 @@ graph = pose2SLAMGraph;
 %% Add prior
 % gaussian for prior
 priorMean = gtsamPose2(0.0, 0.0, 0.0); % prior at origin
-priorNoise = gtsamnoiseModelDiagonal_Sigmas([0.3; 0.3; 0.1]);
+priorNoise = gtsamnoiseModelDiagonal.Sigmas([0.3; 0.3; 0.1]);
 graph.addPosePrior(1, priorMean, priorNoise); % add directly to graph
 
 %% Add odometry
 % general noisemodel for odometry
-odometryNoise = gtsamnoiseModelDiagonal_Sigmas([0.2; 0.2; 0.1]);
+odometryNoise = gtsamnoiseModelDiagonal.Sigmas([0.2; 0.2; 0.1]);
 graph.addRelativePose(1, 2, gtsamPose2(2.0, 0.0, 0.0 ), odometryNoise);
 graph.addRelativePose(2, 3, gtsamPose2(2.0, 0.0, pi/2), odometryNoise);
 graph.addRelativePose(3, 4, gtsamPose2(2.0, 0.0, pi/2), odometryNoise);
 graph.addRelativePose(4, 5, gtsamPose2(2.0, 0.0, pi/2), odometryNoise);
 
 %% Add pose constraint
-model = gtsamnoiseModelDiagonal_Sigmas([0.2; 0.2; 0.1]);
+model = gtsamnoiseModelDiagonal.Sigmas([0.2; 0.2; 0.1]);
 graph.addRelativePose(5, 2, gtsamPose2(2.0, 0.0, pi/2), model);
 
 % print

matlab/examples/Pose2SLAMExample_advanced.m

@@ -25,20 +25,20 @@ graph = pose2SLAMGraph;
 
 %% Add prior
 % gaussian for prior
-priorNoise = gtsamnoiseModelDiagonal_Sigmas([0.3; 0.3; 0.1]);
+priorNoise = gtsamnoiseModelDiagonal.Sigmas([0.3; 0.3; 0.1]);
 priorMean = gtsamPose2(0.0, 0.0, 0.0); % prior at origin
 graph.addPosePrior(1, priorMean, priorNoise); % add directly to graph
 
 %% Add odometry
 % general noisemodel for odometry
-odometryNoise = gtsamnoiseModelDiagonal_Sigmas([0.2; 0.2; 0.1]);
+odometryNoise = gtsamnoiseModelDiagonal.Sigmas([0.2; 0.2; 0.1]);
 odometry = gtsamPose2(2.0, 0.0, 0.0); % create a measurement for both factors (the same in this case)
 graph.addRelativePose(1, 2, odometry, odometryNoise);
 graph.addRelativePose(2, 3, odometry, odometryNoise);
 
 %% Add measurements
 % general noisemodel for measurements
-measurementNoise = gtsamnoiseModelDiagonal_Sigmas([0.1; 0.2]);
+measurementNoise = gtsamnoiseModelDiagonal.Sigmas([0.1; 0.2]);
 
 % print
 graph.print('full graph');

matlab/examples/Pose2SLAMExample_circle.m

@@ -11,7 +11,7 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 %% Create a hexagon of poses
-hexagon = pose2SLAMValues_Circle(6,1.0);
+hexagon = pose2SLAMValues.Circle(6,1.0);
 p0 = hexagon.pose(0);
 p1 = hexagon.pose(1);
 
@@ -19,7 +19,7 @@ p1 = hexagon.pose(1);
 fg = pose2SLAMGraph;
 fg.addPoseConstraint(0, p0);
 delta = p0.between(p1);
-covariance = gtsamnoiseModelDiagonal_Sigmas([0.05; 0.05; 5*pi/180]);
+covariance = gtsamnoiseModelDiagonal.Sigmas([0.05; 0.05; 5*pi/180]);
 fg.addRelativePose(0,1, delta, covariance);
 fg.addRelativePose(1,2, delta, covariance);
 fg.addRelativePose(2,3, delta, covariance);

matlab/examples/Pose2SLAMExample_graph.m

@@ -11,13 +11,13 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 %% Initialize graph, initial estimate, and odometry noise
-model = gtsamnoiseModelDiagonal_Sigmas([0.05; 0.05; 5*pi/180]);
+model = gtsamnoiseModelDiagonal.Sigmas([0.05; 0.05; 5*pi/180]);
 [graph,initial]=load2D('../../examples/Data/w100-odom.graph',model);
 initial.print(sprintf('Initial estimate:\n'));
 
 %% Add a Gaussian prior on pose x_1
 priorMean = gtsamPose2(0.0, 0.0, 0.0); % prior mean is at origin
-priorNoise = gtsamnoiseModelDiagonal_Sigmas([0.01; 0.01; 0.01]);
+priorNoise = gtsamnoiseModelDiagonal.Sigmas([0.01; 0.01; 0.01]);
 graph.addPosePrior(0, priorMean, priorNoise); % add directly to graph
 
 %% Plot Initial Estimate

matlab/examples/Pose2SLAMwSPCG.m

@@ -22,19 +22,19 @@ graph = pose2SLAMGraph;
 %% Add prior
 % gaussian for prior
 priorMean = gtsamPose2(0.0, 0.0, 0.0); % prior at origin
-priorNoise = gtsamnoiseModelDiagonal_Sigmas([0.3; 0.3; 0.1]);
+priorNoise = gtsamnoiseModelDiagonal.Sigmas([0.3; 0.3; 0.1]);
 graph.addPrior(1, priorMean, priorNoise); % add directly to graph
 
 %% Add odometry
 % general noisemodel for odometry
-odometryNoise = gtsamnoiseModelDiagonal_Sigmas([0.2; 0.2; 0.1]);
+odometryNoise = gtsamnoiseModelDiagonal.Sigmas([0.2; 0.2; 0.1]);
 graph.addOdometry(1, 2, gtsamPose2(2.0, 0.0, 0.0 ), odometryNoise);
 graph.addOdometry(2, 3, gtsamPose2(2.0, 0.0, pi/2), odometryNoise);
 graph.addOdometry(3, 4, gtsamPose2(2.0, 0.0, pi/2), odometryNoise);
 graph.addOdometry(4, 5, gtsamPose2(2.0, 0.0, pi/2), odometryNoise);
 
 %% Add pose constraint
-model = gtsamnoiseModelDiagonal_Sigmas([0.2; 0.2; 0.1]);
+model = gtsamnoiseModelDiagonal.Sigmas([0.2; 0.2; 0.1]);
 graph.addRelativePose(5, 2, gtsamPose2(2.0, 0.0, pi/2), model);
 
 % print

matlab/examples/Pose3SLAMExample.m

@@ -11,7 +11,7 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 %% Create a hexagon of poses
-hexagon = pose3SLAMValues_Circle(6,1.0);
+hexagon = pose3SLAMValues.Circle(6,1.0);
 p0 = hexagon.pose(0);
 p1 = hexagon.pose(1);
 
@@ -19,7 +19,7 @@ p1 = hexagon.pose(1);
 fg = pose3SLAMGraph;
 fg.addPoseConstraint(0, p0);
 delta = p0.between(p1);
-covariance = gtsamnoiseModelDiagonal_Sigmas([0.05; 0.05; 0.05; 5*pi/180; 5*pi/180; 5*pi/180]);
+covariance = gtsamnoiseModelDiagonal.Sigmas([0.05; 0.05; 0.05; 5*pi/180; 5*pi/180; 5*pi/180]);
 fg.addRelativePose(0,1, delta, covariance);
 fg.addRelativePose(1,2, delta, covariance);
 fg.addRelativePose(2,3, delta, covariance);

matlab/examples/Pose3SLAMExample_graph.m

@@ -16,7 +16,7 @@ N = 2500;
 filename = '../../examples/Data/sphere2500.txt';
 
 %% Initialize graph, initial estimate, and odometry noise
-model = gtsamnoiseModelDiagonal_Sigmas([0.05; 0.05; 0.05; 5*pi/180; 5*pi/180; 5*pi/180]);
+model = gtsamnoiseModelDiagonal.Sigmas([0.05; 0.05; 0.05; 5*pi/180; 5*pi/180; 5*pi/180]);
 [graph,initial]=load3D(filename,model,true,N);
 
 %% Plot Initial Estimate

matlab/examples/SBAExample.m

@@ -34,7 +34,7 @@ graph = sparseBAGraph;
 
  
 %% Add factors for all measurements
-measurementNoise = gtsamnoiseModelIsotropic_Sigma(2,measurementNoiseSigma);
+measurementNoise = gtsamnoiseModelIsotropic.Sigma(2,measurementNoiseSigma);
 for i=1:length(data.Z)
     for k=1:length(data.Z{i})
         j = data.J{i}{k};
@@ -43,11 +43,11 @@ for i=1:length(data.Z)
 end
 
 %% Add Gaussian priors for a pose and a landmark to constrain the system
-cameraPriorNoise  = gtsamnoiseModelDiagonal_Sigmas(cameraNoiseSigmas);
+cameraPriorNoise  = gtsamnoiseModelDiagonal.Sigmas(cameraNoiseSigmas);
 firstCamera = gtsamSimpleCamera(truth.cameras{1}.pose, truth.K);
 graph.addSimpleCameraPrior(symbol('c',1), firstCamera, cameraPriorNoise);
 
-pointPriorNoise  = gtsamnoiseModelIsotropic_Sigma(3,pointNoiseSigma);
+pointPriorNoise  = gtsamnoiseModelIsotropic.Sigma(3,pointNoiseSigma);
 graph.addPointPrior(symbol('p',1), truth.points{1}, pointPriorNoise);
 
 %% Print the graph

matlab/examples/SFMExample.m

@@ -32,7 +32,7 @@ poseNoiseSigmas = [0.001 0.001 0.001 0.1 0.1 0.1]';
 graph = visualSLAMGraph;
 
 %% Add factors for all measurements
-measurementNoise = gtsamnoiseModelIsotropic_Sigma(2,measurementNoiseSigma);
+measurementNoise = gtsamnoiseModelIsotropic.Sigma(2,measurementNoiseSigma);
 for i=1:length(data.Z)
     for k=1:length(data.Z{i})
         j = data.J{i}{k};
@@ -41,9 +41,9 @@ for i=1:length(data.Z)
 end
 
 %% Add Gaussian priors for a pose and a landmark to constrain the system
-posePriorNoise  = gtsamnoiseModelDiagonal_Sigmas(poseNoiseSigmas);
+posePriorNoise  = gtsamnoiseModelDiagonal.Sigmas(poseNoiseSigmas);
 graph.addPosePrior(symbol('x',1), truth.cameras{1}.pose, posePriorNoise);
-pointPriorNoise  = gtsamnoiseModelIsotropic_Sigma(3,pointNoiseSigma);
+pointPriorNoise  = gtsamnoiseModelIsotropic.Sigma(3,pointNoiseSigma);
 graph.addPointPrior(symbol('p',1), truth.points{1}, pointPriorNoise);
 
 %% Print the graph

matlab/examples/StereoVOExample.m

@@ -31,7 +31,7 @@ graph.addPoseConstraint(x1, first_pose);
 %% Create realistic calibration and measurement noise model
 % format: fx fy skew cx cy baseline
 K = gtsamCal3_S2Stereo(1000, 1000, 0, 320, 240, 0.2);
-stereo_model = gtsamnoiseModelDiagonal_Sigmas([1.0; 1.0; 1.0]);
+stereo_model = gtsamnoiseModelDiagonal.Sigmas([1.0; 1.0; 1.0]);
 
 %% Add measurements
 % pose 1

matlab/examples/StereoVOExample_large.m

@@ -14,7 +14,7 @@
 % format: fx fy skew cx cy baseline
 calib = dlmread('../../examples/Data/VO_calibration.txt');
 K = gtsamCal3_S2Stereo(calib(1), calib(2), calib(3), calib(4), calib(5), calib(6));
-stereo_model = gtsamnoiseModelDiagonal_Sigmas([1.0; 1.0; 1.0]);
+stereo_model = gtsamnoiseModelDiagonal.Sigmas([1.0; 1.0; 1.0]);
 
 %% create empty graph and values
 graph = visualSLAMGraph;

matlab/load3D.m

@@ -29,7 +29,7 @@ for i=1:n
         i=v{2};
         if (~successive & i<N | successive & i==0)
             t = gtsamPoint3(v{3}, v{4}, v{5});
-            R = gtsamRot3_ypr(v{8}, -v{7}, v{6});
+            R = gtsamRot3.Ypr(v{8}, -v{7}, v{6});
             initial.insertPose(i, gtsamPose3(R,t));
         end
     elseif strcmp('EDGE3',line_i(1:5))
@@ -39,7 +39,7 @@ for i=1:n
         if i1<N & i2<N
             if ~successive | abs(i2-i1)==1
                 t = gtsamPoint3(e{4}, e{5}, e{6});
-                R = gtsamRot3_ypr(e{9}, e{8}, e{7});
+                R = gtsamRot3.Ypr(e{9}, e{8}, e{7});
                 dpose = gtsamPose3(R,t);
                 graph.addRelativePose(i1, i2, dpose, model);
                 if successive

matlab/tests/testJacobianFactor.m

@@ -30,7 +30,7 @@ x1 = 3;
 % the RHS
 b2=[-1;1.5;2;-1];
 sigmas = [1;1;1;1];
-model4 = gtsamnoiseModelDiagonal_Sigmas(sigmas);
+model4 = gtsamnoiseModelDiagonal.Sigmas(sigmas);
 combined = gtsamJacobianFactor(x2, Ax2,  l1, Al1, x1, Ax1, b2, model4);
 
 % eliminate the first variable (x2) in the combined factor, destructive !
@@ -69,7 +69,7 @@ Bx1 = [
 % the RHS
 b1= [0.0;0.894427];
 
-model2 = gtsamnoiseModelDiagonal_Sigmas([1;1]);
+model2 = gtsamnoiseModelDiagonal.Sigmas([1;1]);
 expectedLF = gtsamJacobianFactor(l1, Bl1, x1, Bx1, b1, model2);
 
 % check if the result matches the combined (reduced) factor

matlab/tests/testKalmanFilter.m

@@ -22,13 +22,13 @@
 F = eye(2,2);
 B = eye(2,2);
 u = [1.0; 0.0];
-modelQ = gtsamnoiseModelDiagonal_Sigmas([0.1;0.1]);
+modelQ = gtsamnoiseModelDiagonal.Sigmas([0.1;0.1]);
 Q = 0.01*eye(2,2);
 H = eye(2,2);
 z1 = [1.0, 0.0]';
 z2 = [2.0, 0.0]';
 z3 = [3.0, 0.0]';
-modelR = gtsamnoiseModelDiagonal_Sigmas([0.1;0.1]);
+modelR = gtsamnoiseModelDiagonal.Sigmas([0.1;0.1]);
 R = 0.01*eye(2,2);
 
 %% Create the set of expected output TestValues

matlab/tests/testLocalizationExample.m

@@ -15,7 +15,7 @@ graph = pose2SLAMGraph;
 
 %% Add two odometry factors
 odometry = gtsamPose2(2.0, 0.0, 0.0); % create a measurement for both factors (the same in this case)
-odometryNoise = gtsamnoiseModelDiagonal_Sigmas([0.2; 0.2; 0.1]); % 20cm std on x,y, 0.1 rad on theta
+odometryNoise = gtsamnoiseModelDiagonal.Sigmas([0.2; 0.2; 0.1]); % 20cm std on x,y, 0.1 rad on theta
 graph.addRelativePose(1, 2, odometry, odometryNoise);
 graph.addRelativePose(2, 3, odometry, odometryNoise);
 
@@ -25,7 +25,7 @@ groundTruth = pose2SLAMValues;
 groundTruth.insertPose(1, gtsamPose2(0.0, 0.0, 0.0));
 groundTruth.insertPose(2, gtsamPose2(2.0, 0.0, 0.0));
 groundTruth.insertPose(3, gtsamPose2(4.0, 0.0, 0.0));
-noiseModel = gtsamnoiseModelDiagonal_Sigmas([0.1; 0.1; 10]);
+noiseModel = gtsamnoiseModelDiagonal.Sigmas([0.1; 0.1; 10]);
 for i=1:3
     graph.addPosePrior(i, groundTruth.pose(i), noiseModel);
 end

matlab/tests/testOdometryExample.m

@@ -15,12 +15,12 @@ graph = pose2SLAMGraph;
 
 %% Add a Gaussian prior on pose x_1
 priorMean = gtsamPose2(0.0, 0.0, 0.0); % prior mean is at origin
-priorNoise = gtsamnoiseModelDiagonal_Sigmas([0.3; 0.3; 0.1]); % 30cm std on x,y, 0.1 rad on theta
+priorNoise = gtsamnoiseModelDiagonal.Sigmas([0.3; 0.3; 0.1]); % 30cm std on x,y, 0.1 rad on theta
 graph.addPosePrior(1, priorMean, priorNoise); % add directly to graph
 
 %% Add two odometry factors
 odometry = gtsamPose2(2.0, 0.0, 0.0); % create a measurement for both factors (the same in this case)
-odometryNoise = gtsamnoiseModelDiagonal_Sigmas([0.2; 0.2; 0.1]); % 20cm std on x,y, 0.1 rad on theta
+odometryNoise = gtsamnoiseModelDiagonal.Sigmas([0.2; 0.2; 0.1]); % 20cm std on x,y, 0.1 rad on theta
 graph.addRelativePose(1, 2, odometry, odometryNoise);
 graph.addRelativePose(2, 3, odometry, odometryNoise);
 

matlab/tests/testPlanarSLAMExample.m

@@ -28,18 +28,18 @@ graph = planarSLAMGraph;
 
 %% Add prior
 priorMean = gtsamPose2(0.0, 0.0, 0.0); % prior at origin
-priorNoise = gtsamnoiseModelDiagonal_Sigmas([0.3; 0.3; 0.1]);
+priorNoise = gtsamnoiseModelDiagonal.Sigmas([0.3; 0.3; 0.1]);
 graph.addPosePrior(i1, priorMean, priorNoise); % add directly to graph
 
 %% Add odometry
 odometry = gtsamPose2(2.0, 0.0, 0.0);
-odometryNoise = gtsamnoiseModelDiagonal_Sigmas([0.2; 0.2; 0.1]);
+odometryNoise = gtsamnoiseModelDiagonal.Sigmas([0.2; 0.2; 0.1]);
 graph.addRelativePose(i1, i2, odometry, odometryNoise);
 graph.addRelativePose(i2, i3, odometry, odometryNoise);
 
 %% Add bearing/range measurement factors
 degrees = pi/180;
-noiseModel = gtsamnoiseModelDiagonal_Sigmas([0.1; 0.2]);
+noiseModel = gtsamnoiseModelDiagonal.Sigmas([0.1; 0.2]);
 graph.addBearingRange(i1, j1, gtsamRot2(45*degrees), sqrt(4+4), noiseModel);
 graph.addBearingRange(i2, j1, gtsamRot2(90*degrees), 2, noiseModel);
 graph.addBearingRange(i3, j2, gtsamRot2(90*degrees), 2, noiseModel);

matlab/tests/testPose2SLAMExample.m

@@ -24,19 +24,19 @@ graph = pose2SLAMGraph;
 %% Add prior
 % gaussian for prior
 priorMean = gtsamPose2(0.0, 0.0, 0.0); % prior at origin
-priorNoise = gtsamnoiseModelDiagonal_Sigmas([0.3; 0.3; 0.1]);
+priorNoise = gtsamnoiseModelDiagonal.Sigmas([0.3; 0.3; 0.1]);
 graph.addPosePrior(1, priorMean, priorNoise); % add directly to graph
 
 %% Add odometry
 % general noisemodel for odometry
-odometryNoise = gtsamnoiseModelDiagonal_Sigmas([0.2; 0.2; 0.1]);
+odometryNoise = gtsamnoiseModelDiagonal.Sigmas([0.2; 0.2; 0.1]);
 graph.addRelativePose(1, 2, gtsamPose2(2.0, 0.0, 0.0 ), odometryNoise);
 graph.addRelativePose(2, 3, gtsamPose2(2.0, 0.0, pi/2), odometryNoise);
 graph.addRelativePose(3, 4, gtsamPose2(2.0, 0.0, pi/2), odometryNoise);
 graph.addRelativePose(4, 5, gtsamPose2(2.0, 0.0, pi/2), odometryNoise);
 
 %% Add pose constraint
-model = gtsamnoiseModelDiagonal_Sigmas([0.2; 0.2; 0.1]);
+model = gtsamnoiseModelDiagonal.Sigmas([0.2; 0.2; 0.1]);
 graph.addRelativePose(5, 2, gtsamPose2(2.0, 0.0, pi/2), model);
 
 %% Initialize to noisy points

matlab/tests/testPose3SLAMExample.m

@@ -11,7 +11,7 @@
 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
 
 %% Create a hexagon of poses
-hexagon = pose3SLAMValues_Circle(6,1.0);
+hexagon = pose3SLAMValues.Circle(6,1.0);
 p0 = hexagon.pose(0);
 p1 = hexagon.pose(1);
 
@@ -19,7 +19,7 @@ p1 = hexagon.pose(1);
 fg = pose3SLAMGraph;
 fg.addPoseConstraint(0, p0);
 delta = p0.between(p1);
-covariance = gtsamnoiseModelDiagonal_Sigmas([0.05; 0.05; 0.05; 5*pi/180; 5*pi/180; 5*pi/180]);
+covariance = gtsamnoiseModelDiagonal.Sigmas([0.05; 0.05; 0.05; 5*pi/180; 5*pi/180; 5*pi/180]);
 fg.addRelativePose(0,1, delta, covariance);
 fg.addRelativePose(1,2, delta, covariance);
 fg.addRelativePose(2,3, delta, covariance);

matlab/tests/testSFMExample.m

@@ -23,7 +23,7 @@ poseNoiseSigmas = [0.001 0.001 0.001 0.1 0.1 0.1]';
 graph = visualSLAMGraph;
 
 %% Add factors for all measurements
-measurementNoise = gtsamnoiseModelIsotropic_Sigma(2,measurementNoiseSigma);
+measurementNoise = gtsamnoiseModelIsotropic.Sigma(2,measurementNoiseSigma);
 for i=1:length(data.Z)
     for k=1:length(data.Z{i})
         j = data.J{i}{k};
@@ -31,9 +31,9 @@ for i=1:length(data.Z)
     end
 end
 
-posePriorNoise  = gtsamnoiseModelDiagonal_Sigmas(poseNoiseSigmas);
+posePriorNoise  = gtsamnoiseModelDiagonal.Sigmas(poseNoiseSigmas);
 graph.addPosePrior(symbol('x',1), truth.cameras{1}.pose, posePriorNoise);
-pointPriorNoise  = gtsamnoiseModelIsotropic_Sigma(3,pointNoiseSigma);
+pointPriorNoise  = gtsamnoiseModelIsotropic.Sigma(3,pointNoiseSigma);
 graph.addPointPrior(symbol('p',1), truth.points{1}, pointPriorNoise);
 
 %% Initial estimate

matlab/tests/testStereoVOExample.m

@@ -31,7 +31,7 @@ graph.addPoseConstraint(x1, first_pose);
 %% Create realistic calibration and measurement noise model
 % format: fx fy skew cx cy baseline
 K = gtsamCal3_S2Stereo(1000, 1000, 0, 320, 240, 0.2);
-stereo_model = gtsamnoiseModelDiagonal_Sigmas([1.0; 1.0; 1.0]);
+stereo_model = gtsamnoiseModelDiagonal.Sigmas([1.0; 1.0; 1.0]);
 
 %% Add measurements
 % pose 1