72 lines
2.5 KiB
Matlab
72 lines
2.5 KiB
Matlab
function [noiseModels,isam,result,nextPoseIndex] = VisualISAMInitialize(data,truth,options)
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% VisualISAMInitialize initializes visualSLAM::iSAM object and noise parameters
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% Authors: Duy Nguyen Ta, Frank Dellaert and Alex Cunningham
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import gtsam.*
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%% Initialize iSAM
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params = gtsam.ISAM2Params;
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if options.alwaysRelinearize
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params.setRelinearizeSkip(1);
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end
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isam = ISAM2(params);
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%% Set Noise parameters
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noiseModels.pose = noiseModel.Diagonal.Sigmas([0.001 0.001 0.001 0.1 0.1 0.1]');
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%noiseModels.odometry = noiseModel.Diagonal.Sigmas([0.001 0.001 0.001 0.1 0.1 0.1]');
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noiseModels.odometry = noiseModel.Diagonal.Sigmas([0.05 0.05 0.05 0.2 0.2 0.2]');
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noiseModels.point = noiseModel.Isotropic.Sigma(3, 0.1);
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noiseModels.measurement = noiseModel.Isotropic.Sigma(2, 1.0);
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%% Add constraints/priors
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% TODO: should not be from ground truth!
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newFactors = NonlinearFactorGraph;
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initialEstimates = Values;
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for i=1:2
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ii = symbol('x',i);
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if i==1
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if options.hardConstraint % add hard constraint
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newFactors.add(NonlinearEqualityPose3(ii,truth.cameras{1}.pose));
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else
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newFactors.add(PriorFactorPose3(ii,truth.cameras{i}.pose, noiseModels.pose));
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end
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end
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initialEstimates.insert(ii,truth.cameras{i}.pose);
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end
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nextPoseIndex = 3;
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%% Add visual measurement factors from two first poses and initialize observed landmarks
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for i=1:2
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ii = symbol('x',i);
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for k=1:length(data.Z{i})
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j = data.J{i}{k};
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jj = symbol('l',data.J{i}{k});
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newFactors.add(GenericProjectionFactorCal3_S2(data.Z{i}{k}, noiseModels.measurement, ii, jj, data.K));
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% TODO: initial estimates should not be from ground truth!
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if ~initialEstimates.exists(jj)
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initialEstimates.insert(jj, truth.points{j});
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end
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if options.pointPriors % add point priors
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newFactors.add(PriorFactorPoint3(jj, truth.points{j}, noiseModels.point));
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end
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end
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end
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%% Add odometry between frames 1 and 2
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newFactors.add(BetweenFactorPose3(symbol('x',1), symbol('x',2), data.odometry{1}, noiseModels.odometry));
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%% Update ISAM
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if options.batchInitialization % Do a full optimize for first two poses
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batchOptimizer = LevenbergMarquardtOptimizer(newFactors, initialEstimates);
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fullyOptimized = batchOptimizer.optimize();
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isam.update(newFactors, fullyOptimized);
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else
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isam.update(newFactors, initialEstimates);
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end
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% figure(1);tic;
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% t=toc; plot(frame_i,t,'r.'); tic
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result = isam.calculateEstimate();
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% t=toc; plot(frame_i,t,'g.');
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