Cleaned up spurious import statements

matlab/+gtsam/VisualISAMGenerateData.m

@@ -2,8 +2,9 @@ function [data,truth] = VisualISAMGenerateData(options)
 % VisualISAMGenerateData creates data for viusalSLAM::iSAM examples
 % Authors: Duy Nguyen Ta and Frank Dellaert
 
-%% Generate simulated data
 import gtsam.*
+
+%% Generate simulated data
 if options.triangle % Create a triangle target, just 3 points on a plane
     nrPoints = 3;
     r = 10;
@@ -24,7 +25,6 @@ else % 3D landmarks as vertices of a cube
 end
 
 %% Create camera cameras on a circle around the triangle
-import gtsam.*
 height = 10; r = 40;
 truth.K = Cal3_S2(500,500,0,640/2,480/2);
 data.K = truth.K;

matlab/+gtsam/VisualISAMInitialize.m

@@ -2,16 +2,16 @@ function [noiseModels,isam,result,nextPoseIndex] = VisualISAMInitialize(data,tru
 % VisualISAMInitialize initializes visualSLAM::iSAM object and noise parameters
 % Authors: Duy Nguyen Ta, Frank Dellaert and Alex Cunningham
 
-%% Initialize iSAM
 import gtsam.*
+
+%% Initialize iSAM
 params = gtsam.ISAM2Params;
 if options.alwaysRelinearize
     params.setRelinearizeSkip(1);
 end
-isam = ISAM2;
+isam = ISAM2(params);
 
 %% Set Noise parameters
-import gtsam.*
 noiseModels.pose = noiseModel.Diagonal.Sigmas([0.001 0.001 0.001 0.1 0.1 0.1]');
 %noiseModels.odometry = noiseModel.Diagonal.Sigmas([0.001 0.001 0.001 0.1 0.1 0.1]');
 noiseModels.odometry = noiseModel.Diagonal.Sigmas([0.05 0.05 0.05 0.2 0.2 0.2]');
@@ -20,7 +20,6 @@ noiseModels.measurement = noiseModel.Isotropic.Sigma(2, 1.0);
 
 %% Add constraints/priors
 % TODO: should not be from ground truth!
-import gtsam.*
 newFactors = NonlinearFactorGraph;
 initialEstimates = Values;
 for i=1:2
@@ -38,7 +37,6 @@ end
 nextPoseIndex = 3;
 
 %% Add visual measurement factors from two first poses and initialize observed landmarks
-import gtsam.*
 for i=1:2
     ii = symbol('x',i);
     for k=1:length(data.Z{i})
@@ -56,11 +54,9 @@ for i=1:2
 end
 
 %% Add odometry between frames 1 and 2
-import gtsam.*
 newFactors.add(BetweenFactorPose3(symbol('x',1), symbol('x',2), data.odometry{1}, noiseModels.odometry));
 
 %% Update ISAM
-import gtsam.*
 if options.batchInitialization % Do a full optimize for first two poses
     batchOptimizer = LevenbergMarquardtOptimizer(newFactors, initialEstimates);
     fullyOptimized = batchOptimizer.optimize();

matlab/+gtsam/VisualISAMPlot.m

@@ -2,6 +2,7 @@ function VisualISAMPlot(truth, data, isam, result, options)
 % VisualISAMPlot plots current state of ISAM2 object
 % Authors: Duy Nguyen Ta and Frank Dellaert
 
+import gtsam.*
 h=gca;
 cla(h);
 hold on;
@@ -12,7 +13,6 @@ marginals = Marginals(isam.getFactorsUnsafe(), isam.calculateEstimate()); % TODO
 gtsam.plot3DPoints(result, [], marginals);
 
 %% Plot cameras
-import gtsam.*
 M = 1;
 while result.exists(symbol('x',M))
     ii = symbol('x',M);

matlab/+gtsam/VisualISAMStep.m

@@ -2,19 +2,18 @@ function [isam,result,nextPoseIndex] = VisualISAMStep(data,noiseModels,isam,resu
 % VisualISAMStep executes one update step of visualSLAM::iSAM object
 % Authors: Duy Nguyen Ta and Frank Dellaert
 
+import gtsam.*
+
 % iSAM expects us to give it a new set of factors 
 % along with initial estimates for any new variables introduced.
-import gtsam.*
 newFactors = NonlinearFactorGraph;
 initialEstimates = Values;
 
 %% Add odometry
-import gtsam.*
 odometry = data.odometry{nextPoseIndex-1};
 newFactors.add(BetweenFactorPose3(symbol('x',nextPoseIndex-1), symbol('x',nextPoseIndex), odometry, noiseModels.odometry));
 
 %% Add visual measurement factors and initializations as necessary
-import gtsam.*
 for k=1:length(data.Z{nextPoseIndex})
     zij = data.Z{nextPoseIndex}{k};
     j = data.J{nextPoseIndex}{k};
@@ -28,7 +27,6 @@ for k=1:length(data.Z{nextPoseIndex})
 end
 
 %% Initial estimates for the new pose.
-import gtsam.*
 prevPose = result.at(symbol('x',nextPoseIndex-1));
 initialEstimates.insert(symbol('x',nextPoseIndex), prevPose.compose(odometry));